ceres-solver / ceres-solver / 6768b3586a027bb850c0a50e2a27380f5d80142a / . / docs / source / nnls_solving.rst

.. default-domain:: cpp | |

.. cpp:namespace:: ceres | |

.. _chapter-nnls_solving: | |

================================ | |

Solving Non-linear Least Squares | |

================================ | |

Introduction | |

============ | |

Effective use of Ceres requires some familiarity with the basic | |

components of a non-linear least squares solver, so before we describe | |

how to configure and use the solver, we will take a brief look at how | |

some of the core optimization algorithms in Ceres work. | |

Let :math:`x \in \mathbb{R}^n` be an :math:`n`-dimensional vector of | |

variables, and | |

:math:`F(x) = \left[f_1(x), ... , f_{m}(x) \right]^{\top}` be a | |

:math:`m`-dimensional function of :math:`x`. We are interested in | |

solving the optimization problem [#f1]_ | |

.. math:: \arg \min_x \frac{1}{2}\|F(x)\|^2\ . \\ | |

L \le x \le U | |

:label: nonlinsq | |

Where, :math:`L` and :math:`U` are lower and upper bounds on the | |

parameter vector :math:`x`. | |

Since the efficient global minimization of :eq:`nonlinsq` for | |

general :math:`F(x)` is an intractable problem, we will have to settle | |

for finding a local minimum. | |

In the following, the Jacobian :math:`J(x)` of :math:`F(x)` is an | |

:math:`m\times n` matrix, where :math:`J_{ij}(x) = \partial_j f_i(x)` | |

and the gradient vector is :math:`g(x) = \nabla \frac{1}{2}\|F(x)\|^2 | |

= J(x)^\top F(x)`. | |

The general strategy when solving non-linear optimization problems is | |

to solve a sequence of approximations to the original problem | |

[NocedalWright]_. At each iteration, the approximation is solved to | |

determine a correction :math:`\Delta x` to the vector :math:`x`. For | |

non-linear least squares, an approximation can be constructed by using | |

the linearization :math:`F(x+\Delta x) \approx F(x) + J(x)\Delta x`, | |

which leads to the following linear least squares problem: | |

.. math:: \min_{\Delta x} \frac{1}{2}\|J(x)\Delta x + F(x)\|^2 | |

:label: linearapprox | |

Unfortunately, naively solving a sequence of these problems and | |

updating :math:`x \leftarrow x+ \Delta x` leads to an algorithm that | |

may not converge. To get a convergent algorithm, we need to control | |

the size of the step :math:`\Delta x`. Depending on how the size of | |

the step :math:`\Delta x` is controlled, non-linear optimization | |

algorithms can be divided into two major categories [NocedalWright]_. | |

1. **Trust Region** The trust region approach approximates the | |

objective function using using a model function (often a quadratic) | |

over a subset of the search space known as the trust region. If the | |

model function succeeds in minimizing the true objective function | |

the trust region is expanded; conversely, otherwise it is | |

contracted and the model optimization problem is solved again. | |

2. **Line Search** The line search approach first finds a descent | |

direction along which the objective function will be reduced and | |

then computes a step size that decides how far should move along | |

that direction. The descent direction can be computed by various | |

methods, such as gradient descent, Newton's method and Quasi-Newton | |

method. The step size can be determined either exactly or | |

inexactly. | |

Trust region methods are in some sense dual to line search methods: | |

trust region methods first choose a step size (the size of the trust | |

region) and then a step direction while line search methods first | |

choose a step direction and then a step size. Ceres implements | |

multiple algorithms in both categories. | |

.. _section-trust-region-methods: | |

Trust Region Methods | |

==================== | |

The basic trust region algorithm looks something like this. | |

1. Given an initial point :math:`x` and a trust region radius :math:`\mu`. | |

2. Solve | |

.. math:: | |

\arg \min_{\Delta x}& \frac{1}{2}\|J(x)\Delta x + F(x)\|^2 \\ | |

\text{such that} &\|D(x)\Delta x\|^2 \le \mu\\ | |

&L \le x + \Delta x \le U. | |

3. :math:`\rho = \frac{\displaystyle \|F(x + \Delta x)\|^2 - | |

\|F(x)\|^2}{\displaystyle \|J(x)\Delta x + F(x)\|^2 - | |

\|F(x)\|^2}` | |

4. if :math:`\rho > \epsilon` then :math:`x = x + \Delta x`. | |

5. if :math:`\rho > \eta_1` then :math:`\rho = 2 \rho` | |

6. else if :math:`\rho < \eta_2` then :math:`\rho = 0.5 * \rho` | |

7. Go to 2. | |

Here, :math:`\mu` is the trust region radius, :math:`D(x)` is some | |

matrix used to define a metric on the domain of :math:`F(x)` and | |

:math:`\rho` measures the quality of the step :math:`\Delta x`, i.e., | |

how well did the linear model predict the decrease in the value of the | |

non-linear objective. The idea is to increase or decrease the radius | |

of the trust region depending on how well the linearization predicts | |

the behavior of the non-linear objective, which in turn is reflected | |

in the value of :math:`\rho`. | |

The key computational step in a trust-region algorithm is the solution | |

of the constrained optimization problem | |

.. math:: | |

\arg \min_{\Delta x}& \frac{1}{2}\|J(x)\Delta x + F(x)\|^2 \\ | |

\text{such that} &\|D(x)\Delta x\|^2 \le \mu\\ | |

&L \le x + \Delta x \le U. | |

:label: trp | |

There are a number of different ways of solving this problem, each | |

giving rise to a different concrete trust-region algorithm. Currently, | |

Ceres implements two trust-region algorithms - Levenberg-Marquardt | |

and Dogleg, each of which is augmented with a line search if bounds | |

constraints are present [Kanzow]_. The user can choose between them by | |

setting :member:`Solver::Options::trust_region_strategy_type`. | |

.. rubric:: Footnotes | |

.. [#f1] At the level of the non-linear solver, the block structure is | |

not relevant, therefore our discussion here is in terms of an | |

optimization problem defined over a state vector of size | |

:math:`n`. Similarly the presence of loss functions is also | |

ignored as the problem is internally converted into a pure | |

non-linear least squares problem. | |

.. _section-levenberg-marquardt: | |

Levenberg-Marquardt | |

------------------- | |

The Levenberg-Marquardt algorithm [Levenberg]_ [Marquardt]_ is the | |

most popular algorithm for solving non-linear least squares problems. | |

It was also the first trust region algorithm to be developed | |

[Levenberg]_ [Marquardt]_. Ceres implements an exact step [Madsen]_ | |

and an inexact step variant of the Levenberg-Marquardt algorithm | |

[WrightHolt]_ [NashSofer]_. | |

It can be shown, that the solution to :eq:`trp` can be obtained by | |

solving an unconstrained optimization of the form | |

.. math:: \arg\min_{\Delta x}& \frac{1}{2}\|J(x)\Delta x + F(x)\|^2 +\lambda \|D(x)\Delta x\|^2 | |

Where, :math:`\lambda` is a Lagrange multiplier that is inverse | |

related to :math:`\mu`. In Ceres, we solve for | |

.. math:: \arg\min_{\Delta x}& \frac{1}{2}\|J(x)\Delta x + F(x)\|^2 + \frac{1}{\mu} \|D(x)\Delta x\|^2 | |

:label: lsqr | |

The matrix :math:`D(x)` is a non-negative diagonal matrix, typically | |

the square root of the diagonal of the matrix :math:`J(x)^\top J(x)`. | |

Before going further, let us make some notational simplifications. We | |

will assume that the matrix :math:`\sqrt{\mu} D` has been concatenated | |

at the bottom of the matrix :math:`J` and similarly a vector of zeros | |

has been added to the bottom of the vector :math:`f` and the rest of | |

our discussion will be in terms of :math:`J` and :math:`f`, i.e, the | |

linear least squares problem. | |

.. math:: \min_{\Delta x} \frac{1}{2} \|J(x)\Delta x + f(x)\|^2 . | |

:label: simple | |

For all but the smallest problems the solution of :eq:`simple` in | |

each iteration of the Levenberg-Marquardt algorithm is the dominant | |

computational cost in Ceres. Ceres provides a number of different | |

options for solving :eq:`simple`. There are two major classes of | |

methods - factorization and iterative. | |

The factorization methods are based on computing an exact solution of | |

:eq:`lsqr` using a Cholesky or a QR factorization and lead to an exact | |

step Levenberg-Marquardt algorithm. But it is not clear if an exact | |

solution of :eq:`lsqr` is necessary at each step of the LM algorithm | |

to solve :eq:`nonlinsq`. In fact, we have already seen evidence | |

that this may not be the case, as :eq:`lsqr` is itself a regularized | |

version of :eq:`linearapprox`. Indeed, it is possible to | |

construct non-linear optimization algorithms in which the linearized | |

problem is solved approximately. These algorithms are known as inexact | |

Newton or truncated Newton methods [NocedalWright]_. | |

An inexact Newton method requires two ingredients. First, a cheap | |

method for approximately solving systems of linear | |

equations. Typically an iterative linear solver like the Conjugate | |

Gradients method is used for this | |

purpose [NocedalWright]_. Second, a termination rule for | |

the iterative solver. A typical termination rule is of the form | |

.. math:: \|H(x) \Delta x + g(x)\| \leq \eta_k \|g(x)\|. | |

:label: inexact | |

Here, :math:`k` indicates the Levenberg-Marquardt iteration number and | |

:math:`0 < \eta_k <1` is known as the forcing sequence. [WrightHolt]_ | |

prove that a truncated Levenberg-Marquardt algorithm that uses an | |

inexact Newton step based on :eq:`inexact` converges for any | |

sequence :math:`\eta_k \leq \eta_0 < 1` and the rate of convergence | |

depends on the choice of the forcing sequence :math:`\eta_k`. | |

Ceres supports both exact and inexact step solution strategies. When | |

the user chooses a factorization based linear solver, the exact step | |

Levenberg-Marquardt algorithm is used. When the user chooses an | |

iterative linear solver, the inexact step Levenberg-Marquardt | |

algorithm is used. | |

.. _section-dogleg: | |

Dogleg | |

------ | |

Another strategy for solving the trust region problem :eq:`trp` was | |

introduced by M. J. D. Powell. The key idea there is to compute two | |

vectors | |

.. math:: | |

\Delta x^{\text{Gauss-Newton}} &= \arg \min_{\Delta x}\frac{1}{2} \|J(x)\Delta x + f(x)\|^2.\\ | |

\Delta x^{\text{Cauchy}} &= -\frac{\|g(x)\|^2}{\|J(x)g(x)\|^2}g(x). | |

Note that the vector :math:`\Delta x^{\text{Gauss-Newton}}` is the | |

solution to :eq:`linearapprox` and :math:`\Delta | |

x^{\text{Cauchy}}` is the vector that minimizes the linear | |

approximation if we restrict ourselves to moving along the direction | |

of the gradient. Dogleg methods finds a vector :math:`\Delta x` | |

defined by :math:`\Delta x^{\text{Gauss-Newton}}` and :math:`\Delta | |

x^{\text{Cauchy}}` that solves the trust region problem. Ceres | |

supports two variants that can be chose by setting | |

:member:`Solver::Options::dogleg_type`. | |

``TRADITIONAL_DOGLEG`` as described by Powell, constructs two line | |

segments using the Gauss-Newton and Cauchy vectors and finds the point | |

farthest along this line shaped like a dogleg (hence the name) that is | |

contained in the trust-region. For more details on the exact reasoning | |

and computations, please see Madsen et al [Madsen]_. | |

``SUBSPACE_DOGLEG`` is a more sophisticated method that considers the | |

entire two dimensional subspace spanned by these two vectors and finds | |

the point that minimizes the trust region problem in this subspace | |

[ByrdSchnabel]_. | |

The key advantage of the Dogleg over Levenberg-Marquardt is that if | |

the step computation for a particular choice of :math:`\mu` does not | |

result in sufficient decrease in the value of the objective function, | |

Levenberg-Marquardt solves the linear approximation from scratch with | |

a smaller value of :math:`\mu`. Dogleg on the other hand, only needs | |

to compute the interpolation between the Gauss-Newton and the Cauchy | |

vectors, as neither of them depend on the value of :math:`\mu`. | |

The Dogleg method can only be used with the exact factorization based | |

linear solvers. | |

.. _section-inner-iterations: | |

Inner Iterations | |

---------------- | |

Some non-linear least squares problems have additional structure in | |

the way the parameter blocks interact that it is beneficial to modify | |

the way the trust region step is computed. For example, consider the | |

following regression problem | |

.. math:: y = a_1 e^{b_1 x} + a_2 e^{b_3 x^2 + c_1} | |

Given a set of pairs :math:`\{(x_i, y_i)\}`, the user wishes to estimate | |

:math:`a_1, a_2, b_1, b_2`, and :math:`c_1`. | |

Notice that the expression on the left is linear in :math:`a_1` and | |

:math:`a_2`, and given any value for :math:`b_1, b_2` and :math:`c_1`, | |

it is possible to use linear regression to estimate the optimal values | |

of :math:`a_1` and :math:`a_2`. It's possible to analytically | |

eliminate the variables :math:`a_1` and :math:`a_2` from the problem | |

entirely. Problems like these are known as separable least squares | |

problem and the most famous algorithm for solving them is the Variable | |

Projection algorithm invented by Golub & Pereyra [GolubPereyra]_. | |

Similar structure can be found in the matrix factorization with | |

missing data problem. There the corresponding algorithm is known as | |

Wiberg's algorithm [Wiberg]_. | |

Ruhe & Wedin present an analysis of various algorithms for solving | |

separable non-linear least squares problems and refer to *Variable | |

Projection* as Algorithm I in their paper [RuheWedin]_. | |

Implementing Variable Projection is tedious and expensive. Ruhe & | |

Wedin present a simpler algorithm with comparable convergence | |

properties, which they call Algorithm II. Algorithm II performs an | |

additional optimization step to estimate :math:`a_1` and :math:`a_2` | |

exactly after computing a successful Newton step. | |

This idea can be generalized to cases where the residual is not | |

linear in :math:`a_1` and :math:`a_2`, i.e., | |

.. math:: y = f_1(a_1, e^{b_1 x}) + f_2(a_2, e^{b_3 x^2 + c_1}) | |

In this case, we solve for the trust region step for the full problem, | |

and then use it as the starting point to further optimize just `a_1` | |

and `a_2`. For the linear case, this amounts to doing a single linear | |

least squares solve. For non-linear problems, any method for solving | |

the :math:`a_1` and :math:`a_2` optimization problems will do. The | |

only constraint on :math:`a_1` and :math:`a_2` (if they are two | |

different parameter block) is that they do not co-occur in a residual | |

block. | |

This idea can be further generalized, by not just optimizing | |

:math:`(a_1, a_2)`, but decomposing the graph corresponding to the | |

Hessian matrix's sparsity structure into a collection of | |

non-overlapping independent sets and optimizing each of them. | |

Setting :member:`Solver::Options::use_inner_iterations` to ``true`` | |

enables the use of this non-linear generalization of Ruhe & Wedin's | |

Algorithm II. This version of Ceres has a higher iteration | |

complexity, but also displays better convergence behavior per | |

iteration. | |

Setting :member:`Solver::Options::num_threads` to the maximum number | |

possible is highly recommended. | |

.. _section-non-monotonic-steps: | |

Non-monotonic Steps | |

------------------- | |

Note that the basic trust-region algorithm described in | |

:ref:`section-trust-region-methods` is a descent algorithm in that it | |

only accepts a point if it strictly reduces the value of the objective | |

function. | |

Relaxing this requirement allows the algorithm to be more efficient in | |

the long term at the cost of some local increase in the value of the | |

objective function. | |

This is because allowing for non-decreasing objective function values | |

in a principled manner allows the algorithm to *jump over boulders* as | |

the method is not restricted to move into narrow valleys while | |

preserving its convergence properties. | |

Setting :member:`Solver::Options::use_nonmonotonic_steps` to ``true`` | |

enables the non-monotonic trust region algorithm as described by Conn, | |

Gould & Toint in [Conn]_. | |

Even though the value of the objective function may be larger | |

than the minimum value encountered over the course of the | |

optimization, the final parameters returned to the user are the | |

ones corresponding to the minimum cost over all iterations. | |

The option to take non-monotonic steps is available for all trust | |

region strategies. | |

.. _section-line-search-methods: | |

Line Search Methods | |

=================== | |

The line search method in Ceres Solver cannot handle bounds | |

constraints right now, so it can only be used for solving | |

unconstrained problems. | |

Line search algorithms | |

1. Given an initial point :math:`x` | |

2. :math:`\Delta x = -H^{-1}(x) g(x)` | |

3. :math:`\arg \min_\mu \frac{1}{2} \| F(x + \mu \Delta x) \|^2` | |

4. :math:`x = x + \mu \Delta x` | |

5. Goto 2. | |

Here :math:`H(x)` is some approximation to the Hessian of the | |

objective function, and :math:`g(x)` is the gradient at | |

:math:`x`. Depending on the choice of :math:`H(x)` we get a variety of | |

different search directions :math:`\Delta x`. | |

Step 4, which is a one dimensional optimization or `Line Search` along | |

:math:`\Delta x` is what gives this class of methods its name. | |

Different line search algorithms differ in their choice of the search | |

direction :math:`\Delta x` and the method used for one dimensional | |

optimization along :math:`\Delta x`. The choice of :math:`H(x)` is the | |

primary source of computational complexity in these | |

methods. Currently, Ceres Solver supports three choices of search | |

directions, all aimed at large scale problems. | |

1. ``STEEPEST_DESCENT`` This corresponds to choosing :math:`H(x)` to | |

be the identity matrix. This is not a good search direction for | |

anything but the simplest of the problems. It is only included here | |

for completeness. | |

2. ``NONLINEAR_CONJUGATE_GRADIENT`` A generalization of the Conjugate | |

Gradient method to non-linear functions. The generalization can be | |

performed in a number of different ways, resulting in a variety of | |

search directions. Ceres Solver currently supports | |

``FLETCHER_REEVES``, ``POLAK_RIBIERE`` and ``HESTENES_STIEFEL`` | |

directions. | |

3. ``BFGS`` A generalization of the Secant method to multiple | |

dimensions in which a full, dense approximation to the inverse | |

Hessian is maintained and used to compute a quasi-Newton step | |

[NocedalWright]_. BFGS is currently the best known general | |

quasi-Newton algorithm. | |

4. ``LBFGS`` A limited memory approximation to the full ``BFGS`` | |

method in which the last `M` iterations are used to approximate the | |

inverse Hessian used to compute a quasi-Newton step [Nocedal]_, | |

[ByrdNocedal]_. | |

Currently Ceres Solver supports both a backtracking and interpolation | |

based Armijo line search algorithm, and a sectioning / zoom | |

interpolation (strong) Wolfe condition line search algorithm. | |

However, note that in order for the assumptions underlying the | |

``BFGS`` and ``LBFGS`` methods to be guaranteed to be satisfied the | |

Wolfe line search algorithm should be used. | |

.. _section-linear-solver: | |

LinearSolver | |

============ | |

Recall that in both of the trust-region methods described above, the | |

key computational cost is the solution of a linear least squares | |

problem of the form | |

.. math:: \min_{\Delta x} \frac{1}{2} \|J(x)\Delta x + f(x)\|^2 . | |

:label: simple2 | |

Let :math:`H(x)= J(x)^\top J(x)` and :math:`g(x) = -J(x)^\top | |

f(x)`. For notational convenience let us also drop the dependence on | |

:math:`x`. Then it is easy to see that solving :eq:`simple2` is | |

equivalent to solving the *normal equations*. | |

.. math:: H \Delta x = g | |

:label: normal | |

Ceres provides a number of different options for solving :eq:`normal`. | |

.. _section-qr: | |

``DENSE_QR`` | |

------------ | |

For small problems (a couple of hundred parameters and a few thousand | |

residuals) with relatively dense Jacobians, ``DENSE_QR`` is the method | |

of choice [Bjorck]_. Let :math:`J = QR` be the QR-decomposition of | |

:math:`J`, where :math:`Q` is an orthonormal matrix and :math:`R` is | |

an upper triangular matrix [TrefethenBau]_. Then it can be shown that | |

the solution to :eq:`normal` is given by | |

.. math:: \Delta x^* = -R^{-1}Q^\top f | |

Ceres uses ``Eigen`` 's dense QR factorization routines. | |

.. _section-cholesky: | |

``DENSE_NORMAL_CHOLESKY`` & ``SPARSE_NORMAL_CHOLESKY`` | |

------------------------------------------------------ | |

Large non-linear least square problems are usually sparse. In such | |

cases, using a dense QR factorization is inefficient. Let :math:`H = | |

R^\top R` be the Cholesky factorization of the normal equations, where | |

:math:`R` is an upper triangular matrix, then the solution to | |

:eq:`normal` is given by | |

.. math:: | |

\Delta x^* = R^{-1} R^{-\top} g. | |

The observant reader will note that the :math:`R` in the Cholesky | |

factorization of :math:`H` is the same upper triangular matrix | |

:math:`R` in the QR factorization of :math:`J`. Since :math:`Q` is an | |

orthonormal matrix, :math:`J=QR` implies that :math:`J^\top J = R^\top | |

Q^\top Q R = R^\top R`. There are two variants of Cholesky | |

factorization -- sparse and dense. | |

``DENSE_NORMAL_CHOLESKY`` as the name implies performs a dense | |

Cholesky factorization of the normal equations. Ceres uses | |

``Eigen`` 's dense LDLT factorization routines. | |

``SPARSE_NORMAL_CHOLESKY``, as the name implies performs a sparse | |

Cholesky factorization of the normal equations. This leads to | |

substantial savings in time and memory for large sparse | |

problems. Ceres uses the sparse Cholesky factorization routines in | |

Professor Tim Davis' ``SuiteSparse`` or ``CXSparse`` packages [Chen]_ | |

or the sparse Cholesky factorization algorithm in ``Eigen`` (which | |

incidently is a port of the algorithm implemented inside ``CXSparse``) | |

.. _section-schur: | |

``DENSE_SCHUR`` & ``SPARSE_SCHUR`` | |

---------------------------------- | |

While it is possible to use ``SPARSE_NORMAL_CHOLESKY`` to solve bundle | |

adjustment problems, bundle adjustment problem have a special | |

structure, and a more efficient scheme for solving :eq:`normal` | |

can be constructed. | |

Suppose that the SfM problem consists of :math:`p` cameras and | |

:math:`q` points and the variable vector :math:`x` has the block | |

structure :math:`x = [y_{1}, ... ,y_{p},z_{1}, ... ,z_{q}]`. Where, | |

:math:`y` and :math:`z` correspond to camera and point parameters, | |

respectively. Further, let the camera blocks be of size :math:`c` and | |

the point blocks be of size :math:`s` (for most problems :math:`c` = | |

:math:`6`--`9` and :math:`s = 3`). Ceres does not impose any constancy | |

requirement on these block sizes, but choosing them to be constant | |

simplifies the exposition. | |

A key characteristic of the bundle adjustment problem is that there is | |

no term :math:`f_{i}` that includes two or more point blocks. This in | |

turn implies that the matrix :math:`H` is of the form | |

.. math:: H = \left[ \begin{matrix} B & E\\ E^\top & C \end{matrix} \right]\ , | |

:label: hblock | |

where :math:`B \in \mathbb{R}^{pc\times pc}` is a block sparse matrix | |

with :math:`p` blocks of size :math:`c\times c` and :math:`C \in | |

\mathbb{R}^{qs\times qs}` is a block diagonal matrix with :math:`q` blocks | |

of size :math:`s\times s`. :math:`E \in \mathbb{R}^{pc\times qs}` is a | |

general block sparse matrix, with a block of size :math:`c\times s` | |

for each observation. Let us now block partition :math:`\Delta x = | |

[\Delta y,\Delta z]` and :math:`g=[v,w]` to restate :eq:`normal` | |

as the block structured linear system | |

.. math:: \left[ \begin{matrix} B & E\\ E^\top & C \end{matrix} | |

\right]\left[ \begin{matrix} \Delta y \\ \Delta z | |

\end{matrix} \right] = \left[ \begin{matrix} v\\ w | |

\end{matrix} \right]\ , | |

:label: linear2 | |

and apply Gaussian elimination to it. As we noted above, :math:`C` is | |

a block diagonal matrix, with small diagonal blocks of size | |

:math:`s\times s`. Thus, calculating the inverse of :math:`C` by | |

inverting each of these blocks is cheap. This allows us to eliminate | |

:math:`\Delta z` by observing that :math:`\Delta z = C^{-1}(w - E^\top | |

\Delta y)`, giving us | |

.. math:: \left[B - EC^{-1}E^\top\right] \Delta y = v - EC^{-1}w\ . | |

:label: schur | |

The matrix | |

.. math:: S = B - EC^{-1}E^\top | |

is the Schur complement of :math:`C` in :math:`H`. It is also known as | |

the *reduced camera matrix*, because the only variables | |

participating in :eq:`schur` are the ones corresponding to the | |

cameras. :math:`S \in \mathbb{R}^{pc\times pc}` is a block structured | |

symmetric positive definite matrix, with blocks of size :math:`c\times | |

c`. The block :math:`S_{ij}` corresponding to the pair of images | |

:math:`i` and :math:`j` is non-zero if and only if the two images | |

observe at least one common point. | |

Now, :eq:`linear2` can be solved by first forming :math:`S`, solving for | |

:math:`\Delta y`, and then back-substituting :math:`\Delta y` to | |

obtain the value of :math:`\Delta z`. Thus, the solution of what was | |

an :math:`n\times n`, :math:`n=pc+qs` linear system is reduced to the | |

inversion of the block diagonal matrix :math:`C`, a few matrix-matrix | |

and matrix-vector multiplies, and the solution of block sparse | |

:math:`pc\times pc` linear system :eq:`schur`. For almost all | |

problems, the number of cameras is much smaller than the number of | |

points, :math:`p \ll q`, thus solving :eq:`schur` is | |

significantly cheaper than solving :eq:`linear2`. This is the | |

*Schur complement trick* [Brown]_. | |

This still leaves open the question of solving :eq:`schur`. The | |

method of choice for solving symmetric positive definite systems | |

exactly is via the Cholesky factorization [TrefethenBau]_ and | |

depending upon the structure of the matrix, there are, in general, two | |

options. The first is direct factorization, where we store and factor | |

:math:`S` as a dense matrix [TrefethenBau]_. This method has | |

:math:`O(p^2)` space complexity and :math:`O(p^3)` time complexity and | |

is only practical for problems with up to a few hundred cameras. Ceres | |

implements this strategy as the ``DENSE_SCHUR`` solver. | |

But, :math:`S` is typically a fairly sparse matrix, as most images | |

only see a small fraction of the scene. This leads us to the second | |

option: Sparse Direct Methods. These methods store :math:`S` as a | |

sparse matrix, use row and column re-ordering algorithms to maximize | |

the sparsity of the Cholesky decomposition, and focus their compute | |

effort on the non-zero part of the factorization [Chen]_. Sparse | |

direct methods, depending on the exact sparsity structure of the Schur | |

complement, allow bundle adjustment algorithms to significantly scale | |

up over those based on dense factorization. Ceres implements this | |

strategy as the ``SPARSE_SCHUR`` solver. | |

.. _section-cgnr: | |

``CGNR`` | |

-------- | |

For general sparse problems, if the problem is too large for | |

``CHOLMOD`` or a sparse linear algebra library is not linked into | |

Ceres, another option is the ``CGNR`` solver. This solver uses the | |

Conjugate Gradients solver on the *normal equations*, but without | |

forming the normal equations explicitly. It exploits the relation | |

.. math:: | |

H x = J^\top J x = J^\top(J x) | |

When the user chooses ``ITERATIVE_SCHUR`` as the linear solver, Ceres | |

automatically switches from the exact step algorithm to an inexact | |

step algorithm. | |

.. _section-iterative_schur: | |

``ITERATIVE_SCHUR`` | |

------------------- | |

Another option for bundle adjustment problems is to apply | |

Preconditioned Conjugate Gradients to the reduced camera matrix | |

:math:`S` instead of :math:`H`. One reason to do this is that | |

:math:`S` is a much smaller matrix than :math:`H`, but more | |

importantly, it can be shown that :math:`\kappa(S)\leq \kappa(H)`. | |

Ceres implements Conjugate Gradients on :math:`S` as the | |

``ITERATIVE_SCHUR`` solver. When the user chooses ``ITERATIVE_SCHUR`` | |

as the linear solver, Ceres automatically switches from the exact step | |

algorithm to an inexact step algorithm. | |

The key computational operation when using Conjuagate Gradients is the | |

evaluation of the matrix vector product :math:`Sx` for an arbitrary | |

vector :math:`x`. There are two ways in which this product can be | |

evaluated, and this can be controlled using | |

``Solver::Options::use_explicit_schur_complement``. Depending on the | |

problem at hand, the performance difference between these two methods | |

can be quite substantial. | |

1. **Implicit** This is default. Implicit evaluation is suitable for | |

large problems where the cost of computing and storing the Schur | |

Complement :math:`S` is prohibitive. Because PCG only needs | |

access to :math:`S` via its product with a vector, one way to | |

evaluate :math:`Sx` is to observe that | |

.. math:: x_1 &= E^\top x | |

.. math:: x_2 &= C^{-1} x_1 | |

.. math:: x_3 &= Ex_2\\ | |

.. math:: x_4 &= Bx\\ | |

.. math:: Sx &= x_4 - x_3 | |

:label: schurtrick1 | |

Thus, we can run PCG on :math:`S` with the same computational | |

effort per iteration as PCG on :math:`H`, while reaping the | |

benefits of a more powerful preconditioner. In fact, we do not | |

even need to compute :math:`H`, :eq:`schurtrick1` can be | |

implemented using just the columns of :math:`J`. | |

Equation :eq:`schurtrick1` is closely related to *Domain | |

Decomposition methods* for solving large linear systems that | |

arise in structural engineering and partial differential | |

equations. In the language of Domain Decomposition, each point in | |

a bundle adjustment problem is a domain, and the cameras form the | |

interface between these domains. The iterative solution of the | |

Schur complement then falls within the sub-category of techniques | |

known as Iterative Sub-structuring [Saad]_ [Mathew]_. | |

2. **Explicit** The complexity of implicit matrix-vector product | |

evaluation scales with the number of non-zeros in the | |

Jacobian. For small to medium sized problems, the cost of | |

constructing the Schur Complement is small enough that it is | |

better to construct it explicitly in memory and use it to | |

evaluate the product :math:`Sx`. | |

.. NOTE:: | |

In exact arithmetic, the choice of implicit versus explicit Schur | |

complement would have no impact on solution quality. However, in | |

practice if the Jacobian is poorly conditioned, one may observe | |

(usually small) differences in solution quality. This is a | |

natural consequence of performing computations in finite arithmetic. | |

.. _section-preconditioner: | |

Preconditioner | |

-------------- | |

The convergence rate of Conjugate Gradients for | |

solving :eq:`normal` depends on the distribution of eigenvalues | |

of :math:`H` [Saad]_. A useful upper bound is | |

:math:`\sqrt{\kappa(H)}`, where, :math:`\kappa(H)` is the condition | |

number of the matrix :math:`H`. For most bundle adjustment problems, | |

:math:`\kappa(H)` is high and a direct application of Conjugate | |

Gradients to :eq:`normal` results in extremely poor performance. | |

The solution to this problem is to replace :eq:`normal` with a | |

*preconditioned* system. Given a linear system, :math:`Ax =b` and a | |

preconditioner :math:`M` the preconditioned system is given by | |

:math:`M^{-1}Ax = M^{-1}b`. The resulting algorithm is known as | |

Preconditioned Conjugate Gradients algorithm (PCG) and its worst case | |

complexity now depends on the condition number of the *preconditioned* | |

matrix :math:`\kappa(M^{-1}A)`. | |

The computational cost of using a preconditioner :math:`M` is the cost | |

of computing :math:`M` and evaluating the product :math:`M^{-1}y` for | |

arbitrary vectors :math:`y`. Thus, there are two competing factors to | |

consider: How much of :math:`H`'s structure is captured by :math:`M` | |

so that the condition number :math:`\kappa(HM^{-1})` is low, and the | |

computational cost of constructing and using :math:`M`. The ideal | |

preconditioner would be one for which :math:`\kappa(M^{-1}A) | |

=1`. :math:`M=A` achieves this, but it is not a practical choice, as | |

applying this preconditioner would require solving a linear system | |

equivalent to the unpreconditioned problem. It is usually the case | |

that the more information :math:`M` has about :math:`H`, the more | |

expensive it is use. For example, Incomplete Cholesky factorization | |

based preconditioners have much better convergence behavior than the | |

Jacobi preconditioner, but are also much more expensive. | |

The simplest of all preconditioners is the diagonal or Jacobi | |

preconditioner, i.e., :math:`M=\operatorname{diag}(A)`, which for | |

block structured matrices like :math:`H` can be generalized to the | |

block Jacobi preconditioner. | |

For ``ITERATIVE_SCHUR`` there are two obvious choices for block | |

diagonal preconditioners for :math:`S`. The block diagonal of the | |

matrix :math:`B` [Mandel]_ and the block diagonal :math:`S`, i.e, the | |

block Jacobi preconditioner for :math:`S`. Ceres's implements both of | |

these preconditioners and refers to them as ``JACOBI`` and | |

``SCHUR_JACOBI`` respectively. | |

For bundle adjustment problems arising in reconstruction from | |

community photo collections, more effective preconditioners can be | |

constructed by analyzing and exploiting the camera-point visibility | |

structure of the scene [KushalAgarwal]_. Ceres implements the two | |

visibility based preconditioners described by Kushal & Agarwal as | |

``CLUSTER_JACOBI`` and ``CLUSTER_TRIDIAGONAL``. These are fairly new | |

preconditioners and Ceres' implementation of them is in its early | |

stages and is not as mature as the other preconditioners described | |

above. | |

.. _section-ordering: | |

Ordering | |

-------- | |

The order in which variables are eliminated in a linear solver can | |

have a significant of impact on the efficiency and accuracy of the | |

method. For example when doing sparse Cholesky factorization, there | |

are matrices for which a good ordering will give a Cholesky factor | |

with :math:`O(n)` storage, where as a bad ordering will result in an | |

completely dense factor. | |

Ceres allows the user to provide varying amounts of hints to the | |

solver about the variable elimination ordering to use. This can range | |

from no hints, where the solver is free to decide the best ordering | |

based on the user's choices like the linear solver being used, to an | |

exact order in which the variables should be eliminated, and a variety | |

of possibilities in between. | |

Instances of the :class:`ParameterBlockOrdering` class are used to | |

communicate this information to Ceres. | |

Formally an ordering is an ordered partitioning of the parameter | |

blocks. Each parameter block belongs to exactly one group, and each | |

group has a unique integer associated with it, that determines its | |

order in the set of groups. We call these groups *Elimination Groups* | |

Given such an ordering, Ceres ensures that the parameter blocks in the | |

lowest numbered elimination group are eliminated first, and then the | |

parameter blocks in the next lowest numbered elimination group and so | |

on. Within each elimination group, Ceres is free to order the | |

parameter blocks as it chooses. For example, consider the linear system | |

.. math:: | |

x + y &= 3\\ | |

2x + 3y &= 7 | |

There are two ways in which it can be solved. First eliminating | |

:math:`x` from the two equations, solving for :math:`y` and then back | |

substituting for :math:`x`, or first eliminating :math:`y`, solving | |

for :math:`x` and back substituting for :math:`y`. The user can | |

construct three orderings here. | |

1. :math:`\{0: x\}, \{1: y\}` : Eliminate :math:`x` first. | |

2. :math:`\{0: y\}, \{1: x\}` : Eliminate :math:`y` first. | |

3. :math:`\{0: x, y\}` : Solver gets to decide the elimination order. | |

Thus, to have Ceres determine the ordering automatically using | |

heuristics, put all the variables in the same elimination group. The | |

identity of the group does not matter. This is the same as not | |

specifying an ordering at all. To control the ordering for every | |

variable, create an elimination group per variable, ordering them in | |

the desired order. | |

If the user is using one of the Schur solvers (``DENSE_SCHUR``, | |

``SPARSE_SCHUR``, ``ITERATIVE_SCHUR``) and chooses to specify an | |

ordering, it must have one important property. The lowest numbered | |

elimination group must form an independent set in the graph | |

corresponding to the Hessian, or in other words, no two parameter | |

blocks in in the first elimination group should co-occur in the same | |

residual block. For the best performance, this elimination group | |

should be as large as possible. For standard bundle adjustment | |

problems, this corresponds to the first elimination group containing | |

all the 3d points, and the second containing the all the cameras | |

parameter blocks. | |

If the user leaves the choice to Ceres, then the solver uses an | |

approximate maximum independent set algorithm to identify the first | |

elimination group [LiSaad]_. | |

.. _section-solver-options: | |

:class:`Solver::Options` | |

======================== | |

.. class:: Solver::Options | |

:class:`Solver::Options` controls the overall behavior of the | |

solver. We list the various settings and their default values below. | |

.. function:: bool Solver::Options::IsValid(string* error) const | |

Validate the values in the options struct and returns true on | |

success. If there is a problem, the method returns false with | |

``error`` containing a textual description of the cause. | |

.. member:: MinimizerType Solver::Options::minimizer_type | |

Default: ``TRUST_REGION`` | |

Choose between ``LINE_SEARCH`` and ``TRUST_REGION`` algorithms. See | |

:ref:`section-trust-region-methods` and | |

:ref:`section-line-search-methods` for more details. | |

.. member:: LineSearchDirectionType Solver::Options::line_search_direction_type | |

Default: ``LBFGS`` | |

Choices are ``STEEPEST_DESCENT``, ``NONLINEAR_CONJUGATE_GRADIENT``, | |

``BFGS`` and ``LBFGS``. | |

.. member:: LineSearchType Solver::Options::line_search_type | |

Default: ``WOLFE`` | |

Choices are ``ARMIJO`` and ``WOLFE`` (strong Wolfe conditions). | |

Note that in order for the assumptions underlying the ``BFGS`` and | |

``LBFGS`` line search direction algorithms to be guaranteed to be | |

satisifed, the ``WOLFE`` line search should be used. | |

.. member:: NonlinearConjugateGradientType Solver::Options::nonlinear_conjugate_gradient_type | |

Default: ``FLETCHER_REEVES`` | |

Choices are ``FLETCHER_REEVES``, ``POLAK_RIBIERE`` and | |

``HESTENES_STIEFEL``. | |

.. member:: int Solver::Options::max_lbfs_rank | |

Default: 20 | |

The L-BFGS hessian approximation is a low rank approximation to the | |

inverse of the Hessian matrix. The rank of the approximation | |

determines (linearly) the space and time complexity of using the | |

approximation. Higher the rank, the better is the quality of the | |

approximation. The increase in quality is however is bounded for a | |

number of reasons. | |

1. The method only uses secant information and not actual | |

derivatives. | |

2. The Hessian approximation is constrained to be positive | |

definite. | |

So increasing this rank to a large number will cost time and space | |

complexity without the corresponding increase in solution | |

quality. There are no hard and fast rules for choosing the maximum | |

rank. The best choice usually requires some problem specific | |

experimentation. | |

.. member:: bool Solver::Options::use_approximate_eigenvalue_bfgs_scaling | |

Default: ``false`` | |

As part of the ``BFGS`` update step / ``LBFGS`` right-multiply | |

step, the initial inverse Hessian approximation is taken to be the | |

Identity. However, [Oren]_ showed that using instead :math:`I * | |

\gamma`, where :math:`\gamma` is a scalar chosen to approximate an | |

eigenvalue of the true inverse Hessian can result in improved | |

convergence in a wide variety of cases. Setting | |

``use_approximate_eigenvalue_bfgs_scaling`` to true enables this | |

scaling in ``BFGS`` (before first iteration) and ``LBFGS`` (at each | |

iteration). | |

Precisely, approximate eigenvalue scaling equates to | |

.. math:: \gamma = \frac{y_k' s_k}{y_k' y_k} | |

With: | |

.. math:: y_k = \nabla f_{k+1} - \nabla f_k | |

.. math:: s_k = x_{k+1} - x_k | |

Where :math:`f()` is the line search objective and :math:`x` the | |

vector of parameter values [NocedalWright]_. | |

It is important to note that approximate eigenvalue scaling does | |

**not** *always* improve convergence, and that it can in fact | |

*significantly* degrade performance for certain classes of problem, | |

which is why it is disabled by default. In particular it can | |

degrade performance when the sensitivity of the problem to different | |

parameters varies significantly, as in this case a single scalar | |

factor fails to capture this variation and detrimentally downscales | |

parts of the Jacobian approximation which correspond to | |

low-sensitivity parameters. It can also reduce the robustness of the | |

solution to errors in the Jacobians. | |

.. member:: LineSearchIterpolationType Solver::Options::line_search_interpolation_type | |

Default: ``CUBIC`` | |

Degree of the polynomial used to approximate the objective | |

function. Valid values are ``BISECTION``, ``QUADRATIC`` and | |

``CUBIC``. | |

.. member:: double Solver::Options::min_line_search_step_size | |

The line search terminates if: | |

.. math:: \|\Delta x_k\|_\infty < \text{min_line_search_step_size} | |

where :math:`\|\cdot\|_\infty` refers to the max norm, and | |

:math:`\Delta x_k` is the step change in the parameter values at | |

the :math:`k`-th iteration. | |

.. member:: double Solver::Options::line_search_sufficient_function_decrease | |

Default: ``1e-4`` | |

Solving the line search problem exactly is computationally | |

prohibitive. Fortunately, line search based optimization algorithms | |

can still guarantee convergence if instead of an exact solution, | |

the line search algorithm returns a solution which decreases the | |

value of the objective function sufficiently. More precisely, we | |

are looking for a step size s.t. | |

.. math:: f(\text{step_size}) \le f(0) + \text{sufficient_decrease} * [f'(0) * \text{step_size}] | |

This condition is known as the Armijo condition. | |

.. member:: double Solver::Options::max_line_search_step_contraction | |

Default: ``1e-3`` | |

In each iteration of the line search, | |

.. math:: \text{new_step_size} >= \text{max_line_search_step_contraction} * \text{step_size} | |

Note that by definition, for contraction: | |

.. math:: 0 < \text{max_step_contraction} < \text{min_step_contraction} < 1 | |

.. member:: double Solver::Options::min_line_search_step_contraction | |

Default: ``0.6`` | |

In each iteration of the line search, | |

.. math:: \text{new_step_size} <= \text{min_line_search_step_contraction} * \text{step_size} | |

Note that by definition, for contraction: | |

.. math:: 0 < \text{max_step_contraction} < \text{min_step_contraction} < 1 | |

.. member:: int Solver::Options::max_num_line_search_step_size_iterations | |

Default: ``20`` | |

Maximum number of trial step size iterations during each line | |

search, if a step size satisfying the search conditions cannot be | |

found within this number of trials, the line search will stop. | |

As this is an 'artificial' constraint (one imposed by the user, not | |

the underlying math), if ``WOLFE`` line search is being used, *and* | |

points satisfying the Armijo sufficient (function) decrease | |

condition have been found during the current search (in :math:`<=` | |

``max_num_line_search_step_size_iterations``). Then, the step size | |

with the lowest function value which satisfies the Armijo condition | |

will be returned as the new valid step, even though it does *not* | |

satisfy the strong Wolfe conditions. This behaviour protects | |

against early termination of the optimizer at a sub-optimal point. | |

.. member:: int Solver::Options::max_num_line_search_direction_restarts | |

Default: ``5`` | |

Maximum number of restarts of the line search direction algorithm | |

before terminating the optimization. Restarts of the line search | |

direction algorithm occur when the current algorithm fails to | |

produce a new descent direction. This typically indicates a | |

numerical failure, or a breakdown in the validity of the | |

approximations used. | |

.. member:: double Solver::Options::line_search_sufficient_curvature_decrease | |

Default: ``0.9`` | |

The strong Wolfe conditions consist of the Armijo sufficient | |

decrease condition, and an additional requirement that the | |

step size be chosen s.t. the *magnitude* ('strong' Wolfe | |

conditions) of the gradient along the search direction | |

decreases sufficiently. Precisely, this second condition | |

is that we seek a step size s.t. | |

.. math:: \|f'(\text{step_size})\| <= \text{sufficient_curvature_decrease} * \|f'(0)\| | |

Where :math:`f()` is the line search objective and :math:`f'()` is the derivative | |

of :math:`f` with respect to the step size: :math:`\frac{d f}{d~\text{step size}}`. | |

.. member:: double Solver::Options::max_line_search_step_expansion | |

Default: ``10.0`` | |

During the bracketing phase of a Wolfe line search, the step size | |

is increased until either a point satisfying the Wolfe conditions | |

is found, or an upper bound for a bracket containing a point | |

satisfying the conditions is found. Precisely, at each iteration | |

of the expansion: | |

.. math:: \text{new_step_size} <= \text{max_step_expansion} * \text{step_size} | |

By definition for expansion | |

.. math:: \text{max_step_expansion} > 1.0 | |

.. member:: TrustRegionStrategyType Solver::Options::trust_region_strategy_type | |

Default: ``LEVENBERG_MARQUARDT`` | |

The trust region step computation algorithm used by | |

Ceres. Currently ``LEVENBERG_MARQUARDT`` and ``DOGLEG`` are the two | |

valid choices. See :ref:`section-levenberg-marquardt` and | |

:ref:`section-dogleg` for more details. | |

.. member:: DoglegType Solver::Options::dogleg_type | |

Default: ``TRADITIONAL_DOGLEG`` | |

Ceres supports two different dogleg strategies. | |

``TRADITIONAL_DOGLEG`` method by Powell and the ``SUBSPACE_DOGLEG`` | |

method described by [ByrdSchnabel]_ . See :ref:`section-dogleg` | |

for more details. | |

.. member:: bool Solver::Options::use_nonmonotonic_steps | |

Default: ``false`` | |

Relax the requirement that the trust-region algorithm take strictly | |

decreasing steps. See :ref:`section-non-monotonic-steps` for more | |

details. | |

.. member:: int Solver::Options::max_consecutive_nonmonotonic_steps | |

Default: ``5`` | |

The window size used by the step selection algorithm to accept | |

non-monotonic steps. | |

.. member:: int Solver::Options::max_num_iterations | |

Default: ``50`` | |

Maximum number of iterations for which the solver should run. | |

.. member:: double Solver::Options::max_solver_time_in_seconds | |

Default: ``1e6`` | |

Maximum amount of time for which the solver should run. | |

.. member:: int Solver::Options::num_threads | |

Default: ``1`` | |

Number of threads used by Ceres to evaluate the Jacobian. | |

.. member:: double Solver::Options::initial_trust_region_radius | |

Default: ``1e4`` | |

The size of the initial trust region. When the | |

``LEVENBERG_MARQUARDT`` strategy is used, the reciprocal of this | |

number is the initial regularization parameter. | |

.. member:: double Solver::Options::max_trust_region_radius | |

Default: ``1e16`` | |

The trust region radius is not allowed to grow beyond this value. | |

.. member:: double Solver::Options::min_trust_region_radius | |

Default: ``1e-32`` | |

The solver terminates, when the trust region becomes smaller than | |

this value. | |

.. member:: double Solver::Options::min_relative_decrease | |

Default: ``1e-3`` | |

Lower threshold for relative decrease before a trust-region step is | |

accepted. | |

.. member:: double Solver::Options::min_lm_diagonal | |

Default: ``1e6`` | |

The ``LEVENBERG_MARQUARDT`` strategy, uses a diagonal matrix to | |

regularize the trust region step. This is the lower bound on | |

the values of this diagonal matrix. | |

.. member:: double Solver::Options::max_lm_diagonal | |

Default: ``1e32`` | |

The ``LEVENBERG_MARQUARDT`` strategy, uses a diagonal matrix to | |

regularize the trust region step. This is the upper bound on | |

the values of this diagonal matrix. | |

.. member:: int Solver::Options::max_num_consecutive_invalid_steps | |

Default: ``5`` | |

The step returned by a trust region strategy can sometimes be | |

numerically invalid, usually because of conditioning | |

issues. Instead of crashing or stopping the optimization, the | |

optimizer can go ahead and try solving with a smaller trust | |

region/better conditioned problem. This parameter sets the number | |

of consecutive retries before the minimizer gives up. | |

.. member:: double Solver::Options::function_tolerance | |

Default: ``1e-6`` | |

Solver terminates if | |

.. math:: \frac{|\Delta \text{cost}|}{\text{cost}} < \text{function_tolerance} | |

where, :math:`\Delta \text{cost}` is the change in objective | |

function value (up or down) in the current iteration of | |

Levenberg-Marquardt. | |

.. member:: double Solver::Options::gradient_tolerance | |

Default: ``1e-10`` | |

Solver terminates if | |

.. math:: \|x - \Pi \boxplus(x, -g(x))\|_\infty < \text{gradient_tolerance} | |

where :math:`\|\cdot\|_\infty` refers to the max norm, :math:`\Pi` | |

is projection onto the bounds constraints and :math:`\boxplus` is | |

Plus operation for the overall local parameterization associated | |

with the parameter vector. | |

.. member:: double Solver::Options::parameter_tolerance | |

Default: ``1e-8`` | |

Solver terminates if | |

.. math:: \|\Delta x\| < (\|x\| + \text{parameter_tolerance}) * \text{parameter_tolerance} | |

where :math:`\Delta x` is the step computed by the linear solver in | |

the current iteration of Levenberg-Marquardt. | |

.. member:: LinearSolverType Solver::Options::linear_solver_type | |

Default: ``SPARSE_NORMAL_CHOLESKY`` / ``DENSE_QR`` | |

Type of linear solver used to compute the solution to the linear | |

least squares problem in each iteration of the Levenberg-Marquardt | |

algorithm. If Ceres is built with support for ``SuiteSparse`` or | |

``CXSparse`` or ``Eigen``'s sparse Cholesky factorization, the | |

default is ``SPARSE_NORMAL_CHOLESKY``, it is ``DENSE_QR`` | |

otherwise. | |

.. member:: PreconditionerType Solver::Options::preconditioner_type | |

Default: ``JACOBI`` | |

The preconditioner used by the iterative linear solver. The default | |

is the block Jacobi preconditioner. Valid values are (in increasing | |

order of complexity) ``IDENTITY``, ``JACOBI``, ``SCHUR_JACOBI``, | |

``CLUSTER_JACOBI`` and ``CLUSTER_TRIDIAGONAL``. See | |

:ref:`section-preconditioner` for more details. | |

.. member:: VisibilityClusteringType Solver::Options::visibility_clustering_type | |

Default: ``CANONICAL_VIEWS`` | |

Type of clustering algorithm to use when constructing a visibility | |

based preconditioner. The original visibility based preconditioning | |

paper and implementation only used the canonical views algorithm. | |

This algorithm gives high quality results but for large dense | |

graphs can be particularly expensive. As its worst case complexity | |

is cubic in size of the graph. | |

Another option is to use ``SINGLE_LINKAGE`` which is a simple | |

thresholded single linkage clustering algorithm that only pays | |

attention to tightly coupled blocks in the Schur complement. This | |

is a fast algorithm that works well. | |

The optimal choice of the clustering algorithm depends on the | |

sparsity structure of the problem, but generally speaking we | |

recommend that you try ``CANONICAL_VIEWS`` first and if it is too | |

expensive try ``SINGLE_LINKAGE``. | |

.. member:: DenseLinearAlgebraLibrary Solver::Options::dense_linear_algebra_library_type | |

Default:``EIGEN`` | |

Ceres supports using multiple dense linear algebra libraries for | |

dense matrix factorizations. Currently ``EIGEN`` and ``LAPACK`` are | |

the valid choices. ``EIGEN`` is always available, ``LAPACK`` refers | |

to the system ``BLAS + LAPACK`` library which may or may not be | |

available. | |

This setting affects the ``DENSE_QR``, ``DENSE_NORMAL_CHOLESKY`` | |

and ``DENSE_SCHUR`` solvers. For small to moderate sized probem | |

``EIGEN`` is a fine choice but for large problems, an optimized | |

``LAPACK + BLAS`` implementation can make a substantial difference | |

in performance. | |

.. member:: SparseLinearAlgebraLibrary Solver::Options::sparse_linear_algebra_library_type | |

Default:``SUITE_SPARSE`` | |

Ceres supports the use of three sparse linear algebra libraries, | |

``SuiteSparse``, which is enabled by setting this parameter to | |

``SUITE_SPARSE``, ``CXSparse``, which can be selected by setting | |

this parameter to ```CX_SPARSE`` and ``Eigen`` which is enabled by | |

setting this parameter to ``EIGEN_SPARSE``. | |

``SuiteSparse`` is a sophisticated and complex sparse linear | |

algebra library and should be used in general. | |

If your needs/platforms prevent you from using ``SuiteSparse``, | |

consider using ``CXSparse``, which is a much smaller, easier to | |

build library. As can be expected, its performance on large | |

problems is not comparable to that of ``SuiteSparse``. | |

Last but not the least you can use the sparse linear algebra | |

routines in ``Eigen``. Currently the performance of this library is | |

the poorest of the three. But this should change in the near | |

future. | |

Another thing to consider here is that the sparse Cholesky | |

factorization libraries in Eigen are licensed under ``LGPL`` and | |

building Ceres with support for ``EIGEN_SPARSE`` will result in an | |

LGPL licensed library (since the corresponding code from Eigen is | |

compiled into the library). | |

The upside is that you do not need to build and link to an external | |

library to use ``EIGEN_SPARSE``. | |

.. member:: int Solver::Options::num_linear_solver_threads | |

Default: ``1`` | |

Number of threads used by the linear solver. | |

.. member:: shared_ptr<ParameterBlockOrdering> Solver::Options::linear_solver_ordering | |

Default: ``NULL`` | |

An instance of the ordering object informs the solver about the | |

desired order in which parameter blocks should be eliminated by the | |

linear solvers. See section~\ref{sec:ordering`` for more details. | |

If ``NULL``, the solver is free to choose an ordering that it | |

thinks is best. | |

See :ref:`section-ordering` for more details. | |

.. member:: bool Solver::Options::use_explicit_schur_complement | |

Default: ``false`` | |

Use an explicitly computed Schur complement matrix with | |

``ITERATIVE_SCHUR``. | |

By default this option is disabled and ``ITERATIVE_SCHUR`` | |

evaluates evaluates matrix-vector products between the Schur | |

complement and a vector implicitly by exploiting the algebraic | |

expression for the Schur complement. | |

The cost of this evaluation scales with the number of non-zeros in | |

the Jacobian. | |

For small to medium sized problems there is a sweet spot where | |

computing the Schur complement is cheap enough that it is much more | |

efficient to explicitly compute it and use it for evaluating the | |

matrix-vector products. | |

Enabling this option tells ``ITERATIVE_SCHUR`` to use an explicitly | |

computed Schur complement. This can improve the performance of the | |

``ITERATIVE_SCHUR`` solver significantly. | |

.. NOTE: | |

This option can only be used with the ``SCHUR_JACOBI`` | |

preconditioner. | |

.. member:: bool Solver::Options::use_post_ordering | |

Default: ``false`` | |

Sparse Cholesky factorization algorithms use a fill-reducing | |

ordering to permute the columns of the Jacobian matrix. There are | |

two ways of doing this. | |

1. Compute the Jacobian matrix in some order and then have the | |

factorization algorithm permute the columns of the Jacobian. | |

2. Compute the Jacobian with its columns already permuted. | |

The first option incurs a significant memory penalty. The | |

factorization algorithm has to make a copy of the permuted Jacobian | |

matrix, thus Ceres pre-permutes the columns of the Jacobian matrix | |

and generally speaking, there is no performance penalty for doing | |

so. | |

In some rare cases, it is worth using a more complicated reordering | |

algorithm which has slightly better runtime performance at the | |

expense of an extra copy of the Jacobian matrix. Setting | |

``use_postordering`` to ``true`` enables this tradeoff. | |

.. member:: bool Solver::Options::dynamic_sparsity | |

Some non-linear least squares problems are symbolically dense but | |

numerically sparse. i.e. at any given state only a small number of | |

Jacobian entries are non-zero, but the position and number of | |

non-zeros is different depending on the state. For these problems | |

it can be useful to factorize the sparse jacobian at each solver | |

iteration instead of including all of the zero entries in a single | |

general factorization. | |

If your problem does not have this property (or you do not know), | |

then it is probably best to keep this false, otherwise it will | |

likely lead to worse performance. | |

This setting only affects the `SPARSE_NORMAL_CHOLESKY` solver. | |

.. member:: int Solver::Options::min_linear_solver_iterations | |

Default: ``0`` | |

Minimum number of iterations used by the linear solver. This only | |

makes sense when the linear solver is an iterative solver, e.g., | |

``ITERATIVE_SCHUR`` or ``CGNR``. | |

.. member:: int Solver::Options::max_linear_solver_iterations | |

Default: ``500`` | |

Minimum number of iterations used by the linear solver. This only | |

makes sense when the linear solver is an iterative solver, e.g., | |

``ITERATIVE_SCHUR`` or ``CGNR``. | |

.. member:: double Solver::Options::eta | |

Default: ``1e-1`` | |

Forcing sequence parameter. The truncated Newton solver uses this | |

number to control the relative accuracy with which the Newton step | |

is computed. This constant is passed to | |

``ConjugateGradientsSolver`` which uses it to terminate the | |

iterations when | |

.. math:: \frac{Q_i - Q_{i-1}}{Q_i} < \frac{\eta}{i} | |

.. member:: bool Solver::Options::jacobi_scaling | |

Default: ``true`` | |

``true`` means that the Jacobian is scaled by the norm of its | |

columns before being passed to the linear solver. This improves the | |

numerical conditioning of the normal equations. | |

.. member:: bool Solver::Options::use_inner_iterations | |

Default: ``false`` | |

Use a non-linear version of a simplified variable projection | |

algorithm. Essentially this amounts to doing a further optimization | |

on each Newton/Trust region step using a coordinate descent | |

algorithm. For more details, see :ref:`section-inner-iterations`. | |

.. member:: double Solver::Options::inner_iteration_tolerance | |

Default: ``1e-3`` | |

Generally speaking, inner iterations make significant progress in | |

the early stages of the solve and then their contribution drops | |

down sharply, at which point the time spent doing inner iterations | |

is not worth it. | |

Once the relative decrease in the objective function due to inner | |

iterations drops below ``inner_iteration_tolerance``, the use of | |

inner iterations in subsequent trust region minimizer iterations is | |

disabled. | |

.. member:: shared_ptr<ParameterBlockOrdering> Solver::Options::inner_iteration_ordering | |

Default: ``NULL`` | |

If :member:`Solver::Options::use_inner_iterations` true, then the | |

user has two choices. | |

1. Let the solver heuristically decide which parameter blocks to | |

optimize in each inner iteration. To do this, set | |

:member:`Solver::Options::inner_iteration_ordering` to ``NULL``. | |

2. Specify a collection of of ordered independent sets. The lower | |

numbered groups are optimized before the higher number groups | |

during the inner optimization phase. Each group must be an | |

independent set. Not all parameter blocks need to be included in | |

the ordering. | |

See :ref:`section-ordering` for more details. | |

.. member:: LoggingType Solver::Options::logging_type | |

Default: ``PER_MINIMIZER_ITERATION`` | |

.. member:: bool Solver::Options::minimizer_progress_to_stdout | |

Default: ``false`` | |

By default the :class:`Minimizer` progress is logged to ``STDERR`` | |

depending on the ``vlog`` level. If this flag is set to true, and | |

:member:`Solver::Options::logging_type` is not ``SILENT``, the logging | |

output is sent to ``STDOUT``. | |

For ``TRUST_REGION_MINIMIZER`` the progress display looks like | |

.. code-block:: bash | |

iter cost cost_change |gradient| |step| tr_ratio tr_radius ls_iter iter_time total_time | |

0 4.185660e+06 0.00e+00 1.09e+08 0.00e+00 0.00e+00 1.00e+04 0 7.59e-02 3.37e-01 | |

1 1.062590e+05 4.08e+06 8.99e+06 5.36e+02 9.82e-01 3.00e+04 1 1.65e-01 5.03e-01 | |

2 4.992817e+04 5.63e+04 8.32e+06 3.19e+02 6.52e-01 3.09e+04 1 1.45e-01 6.48e-01 | |

Here | |

#. ``cost`` is the value of the objective function. | |

#. ``cost_change`` is the change in the value of the objective | |

function if the step computed in this iteration is accepted. | |

#. ``|gradient|`` is the max norm of the gradient. | |

#. ``|step|`` is the change in the parameter vector. | |

#. ``tr_ratio`` is the ratio of the actual change in the objective | |

function value to the change in the value of the trust | |

region model. | |

#. ``tr_radius`` is the size of the trust region radius. | |

#. ``ls_iter`` is the number of linear solver iterations used to | |

compute the trust region step. For direct/factorization based | |

solvers it is always 1, for iterative solvers like | |

``ITERATIVE_SCHUR`` it is the number of iterations of the | |

Conjugate Gradients algorithm. | |

#. ``iter_time`` is the time take by the current iteration. | |

#. ``total_time`` is the total time taken by the minimizer. | |

For ``LINE_SEARCH_MINIMIZER`` the progress display looks like | |

.. code-block:: bash | |

0: f: 2.317806e+05 d: 0.00e+00 g: 3.19e-01 h: 0.00e+00 s: 0.00e+00 e: 0 it: 2.98e-02 tt: 8.50e-02 | |

1: f: 2.312019e+05 d: 5.79e+02 g: 3.18e-01 h: 2.41e+01 s: 1.00e+00 e: 1 it: 4.54e-02 tt: 1.31e-01 | |

2: f: 2.300462e+05 d: 1.16e+03 g: 3.17e-01 h: 4.90e+01 s: 2.54e-03 e: 1 it: 4.96e-02 tt: 1.81e-01 | |

Here | |

#. ``f`` is the value of the objective function. | |

#. ``d`` is the change in the value of the objective function if | |

the step computed in this iteration is accepted. | |

#. ``g`` is the max norm of the gradient. | |

#. ``h`` is the change in the parameter vector. | |

#. ``s`` is the optimal step length computed by the line search. | |

#. ``it`` is the time take by the current iteration. | |

#. ``tt`` is the total time taken by the minimizer. | |

.. member:: vector<int> Solver::Options::trust_region_minimizer_iterations_to_dump | |

Default: ``empty`` | |

List of iterations at which the trust region minimizer should dump | |

the trust region problem. Useful for testing and benchmarking. If | |

``empty``, no problems are dumped. | |

.. member:: string Solver::Options::trust_region_problem_dump_directory | |

Default: ``/tmp`` | |

Directory to which the problems should be written to. Should be | |

non-empty if | |

:member:`Solver::Options::trust_region_minimizer_iterations_to_dump` is | |

non-empty and | |

:member:`Solver::Options::trust_region_problem_dump_format_type` is not | |

``CONSOLE``. | |

.. member:: DumpFormatType Solver::Options::trust_region_problem_dump_format | |

Default: ``TEXTFILE`` | |

The format in which trust region problems should be logged when | |

:member:`Solver::Options::trust_region_minimizer_iterations_to_dump` | |

is non-empty. There are three options: | |

* ``CONSOLE`` prints the linear least squares problem in a human | |

readable format to ``stderr``. The Jacobian is printed as a | |

dense matrix. The vectors :math:`D`, :math:`x` and :math:`f` are | |

printed as dense vectors. This should only be used for small | |

problems. | |

* ``TEXTFILE`` Write out the linear least squares problem to the | |

directory pointed to by | |

:member:`Solver::Options::trust_region_problem_dump_directory` as | |

text files which can be read into ``MATLAB/Octave``. The Jacobian | |

is dumped as a text file containing :math:`(i,j,s)` triplets, the | |

vectors :math:`D`, `x` and `f` are dumped as text files | |

containing a list of their values. | |

A ``MATLAB/Octave`` script called | |

``ceres_solver_iteration_???.m`` is also output, which can be | |

used to parse and load the problem into memory. | |

.. member:: bool Solver::Options::check_gradients | |

Default: ``false`` | |

Check all Jacobians computed by each residual block with finite | |

differences. This is expensive since it involves computing the | |

derivative by normal means (e.g. user specified, autodiff, etc), | |

then also computing it using finite differences. The results are | |

compared, and if they differ substantially, details are printed to | |

the log. | |

.. member:: double Solver::Options::gradient_check_relative_precision | |

Default: ``1e08`` | |

Precision to check for in the gradient checker. If the relative | |

difference between an element in a Jacobian exceeds this number, | |

then the Jacobian for that cost term is dumped. | |

.. member:: double Solver::Options::numeric_derivative_relative_step_size | |

Default: ``1e-6`` | |

Relative shift used for taking numeric derivatives. For finite | |

differencing, each dimension is evaluated at slightly shifted | |

values, e.g., for forward differences, the numerical derivative is | |

.. math:: | |

\delta &= numeric\_derivative\_relative\_step\_size\\ | |

\Delta f &= \frac{f((1 + \delta) x) - f(x)}{\delta x} | |

The finite differencing is done along each dimension. The reason to | |

use a relative (rather than absolute) step size is that this way, | |

numeric differentiation works for functions where the arguments are | |

typically large (e.g. :math:`10^9`) and when the values are small | |

(e.g. :math:`10^{-5}`). It is possible to construct *torture cases* | |

which break this finite difference heuristic, but they do not come | |

up often in practice. | |

.. member:: vector<IterationCallback> Solver::Options::callbacks | |

Callbacks that are executed at the end of each iteration of the | |

:class:`Minimizer`. They are executed in the order that they are | |

specified in this vector. By default, parameter blocks are updated | |

only at the end of the optimization, i.e., when the | |

:class:`Minimizer` terminates. This behavior is controlled by | |

:member:`Solver::Options::update_state_every_variable`. If the user | |

wishes to have access to the update parameter blocks when his/her | |

callbacks are executed, then set | |

:member:`Solver::Options::update_state_every_iteration` to true. | |

The solver does NOT take ownership of these pointers. | |

.. member:: bool Solver::Options::update_state_every_iteration | |

Default: ``false`` | |

Normally the parameter blocks are only updated when the solver | |

terminates. Setting this to true update them in every | |

iteration. This setting is useful when building an interactive | |

application using Ceres and using an :class:`IterationCallback`. | |

:class:`ParameterBlockOrdering` | |

=============================== | |

.. class:: ParameterBlockOrdering | |

``ParameterBlockOrdering`` is a class for storing and manipulating | |

an ordered collection of groups/sets with the following semantics: | |

Group IDs are non-negative integer values. Elements are any type | |

that can serve as a key in a map or an element of a set. | |

An element can only belong to one group at a time. A group may | |

contain an arbitrary number of elements. | |

Groups are ordered by their group id. | |

.. function:: bool ParameterBlockOrdering::AddElementToGroup(const double* element, const int group) | |

Add an element to a group. If a group with this id does not exist, | |

one is created. This method can be called any number of times for | |

the same element. Group ids should be non-negative numbers. Return | |

value indicates if adding the element was a success. | |

.. function:: void ParameterBlockOrdering::Clear() | |

Clear the ordering. | |

.. function:: bool ParameterBlockOrdering::Remove(const double* element) | |

Remove the element, no matter what group it is in. If the element | |

is not a member of any group, calling this method will result in a | |

crash. Return value indicates if the element was actually removed. | |

.. function:: void ParameterBlockOrdering::Reverse() | |

Reverse the order of the groups in place. | |

.. function:: int ParameterBlockOrdering::GroupId(const double* element) const | |

Return the group id for the element. If the element is not a member | |

of any group, return -1. | |

.. function:: bool ParameterBlockOrdering::IsMember(const double* element) const | |

True if there is a group containing the parameter block. | |

.. function:: int ParameterBlockOrdering::GroupSize(const int group) const | |

This function always succeeds, i.e., implicitly there exists a | |

group for every integer. | |

.. function:: int ParameterBlockOrdering::NumElements() const | |

Number of elements in the ordering. | |

.. function:: int ParameterBlockOrdering::NumGroups() const | |

Number of groups with one or more elements. | |

:class:`IterationCallback` | |

========================== | |

.. class:: IterationSummary | |

:class:`IterationSummary` describes the state of the minimizer at | |

the end of each iteration. | |

.. member:: int32 IterationSummary::iteration | |

Current iteration number. | |

.. member:: bool IterationSummary::step_is_valid | |

Step was numerically valid, i.e., all values are finite and the | |

step reduces the value of the linearized model. | |

**Note**: :member:`IterationSummary::step_is_valid` is `false` | |

when :member:`IterationSummary::iteration` = 0. | |

.. member:: bool IterationSummary::step_is_nonmonotonic | |

Step did not reduce the value of the objective function | |

sufficiently, but it was accepted because of the relaxed | |

acceptance criterion used by the non-monotonic trust region | |

algorithm. | |

**Note**: :member:`IterationSummary::step_is_nonmonotonic` is | |

`false` when when :member:`IterationSummary::iteration` = 0. | |

.. member:: bool IterationSummary::step_is_successful | |

Whether or not the minimizer accepted this step or not. | |

If the ordinary trust region algorithm is used, this means that the | |

relative reduction in the objective function value was greater than | |

:member:`Solver::Options::min_relative_decrease`. However, if the | |

non-monotonic trust region algorithm is used | |

(:member:`Solver::Options::use_nonmonotonic_steps` = `true`), then | |

even if the relative decrease is not sufficient, the algorithm may | |

accept the step and the step is declared successful. | |

**Note**: :member:`IterationSummary::step_is_successful` is `false` | |

when when :member:`IterationSummary::iteration` = 0. | |

.. member:: double IterationSummary::cost | |

Value of the objective function. | |

.. member:: double IterationSummary::cost_change | |

Change in the value of the objective function in this | |

iteration. This can be positive or negative. | |

.. member:: double IterationSummary::gradient_max_norm | |

Infinity norm of the gradient vector. | |

.. member:: double IterationSummary::gradient_norm | |

2-norm of the gradient vector. | |

.. member:: double IterationSummary::step_norm | |

2-norm of the size of the step computed in this iteration. | |

.. member:: double IterationSummary::relative_decrease | |

For trust region algorithms, the ratio of the actual change in cost | |

and the change in the cost of the linearized approximation. | |

This field is not used when a linear search minimizer is used. | |

.. member:: double IterationSummary::trust_region_radius | |

Size of the trust region at the end of the current iteration. For | |

the Levenberg-Marquardt algorithm, the regularization parameter is | |

1.0 / member::`IterationSummary::trust_region_radius`. | |

.. member:: double IterationSummary::eta | |

For the inexact step Levenberg-Marquardt algorithm, this is the | |

relative accuracy with which the step is solved. This number is | |

only applicable to the iterative solvers capable of solving linear | |

systems inexactly. Factorization-based exact solvers always have an | |

eta of 0.0. | |

.. member:: double IterationSummary::step_size | |

Step sized computed by the line search algorithm. | |

This field is not used when a trust region minimizer is used. | |

.. member:: int IterationSummary::line_search_function_evaluations | |

Number of function evaluations used by the line search algorithm. | |

This field is not used when a trust region minimizer is used. | |

.. member:: int IterationSummary::linear_solver_iterations | |

Number of iterations taken by the linear solver to solve for the | |

trust region step. | |

Currently this field is not used when a line search minimizer is | |

used. | |

.. member:: double IterationSummary::iteration_time_in_seconds | |

Time (in seconds) spent inside the minimizer loop in the current | |

iteration. | |

.. member:: double IterationSummary::step_solver_time_in_seconds | |

Time (in seconds) spent inside the trust region step solver. | |

.. member:: double IterationSummary::cumulative_time_in_seconds | |

Time (in seconds) since the user called Solve(). | |

.. class:: IterationCallback | |

Interface for specifying callbacks that are executed at the end of | |

each iteration of the minimizer. | |

.. code-block:: c++ | |

class IterationCallback { | |

public: | |

virtual ~IterationCallback() {} | |

virtual CallbackReturnType operator()(const IterationSummary& summary) = 0; | |

}; | |

The solver uses the return value of ``operator()`` to decide whether | |

to continue solving or to terminate. The user can return three | |

values. | |

#. ``SOLVER_ABORT`` indicates that the callback detected an abnormal | |

situation. The solver returns without updating the parameter | |

blocks (unless ``Solver::Options::update_state_every_iteration`` is | |

set true). Solver returns with ``Solver::Summary::termination_type`` | |

set to ``USER_FAILURE``. | |

#. ``SOLVER_TERMINATE_SUCCESSFULLY`` indicates that there is no need | |

to optimize anymore (some user specified termination criterion | |

has been met). Solver returns with | |

``Solver::Summary::termination_type``` set to ``USER_SUCCESS``. | |

#. ``SOLVER_CONTINUE`` indicates that the solver should continue | |

optimizing. | |

For example, the following :class:`IterationCallback` is used | |

internally by Ceres to log the progress of the optimization. | |

.. code-block:: c++ | |

class LoggingCallback : public IterationCallback { | |

public: | |

explicit LoggingCallback(bool log_to_stdout) | |

: log_to_stdout_(log_to_stdout) {} | |

~LoggingCallback() {} | |

CallbackReturnType operator()(const IterationSummary& summary) { | |

const char* kReportRowFormat = | |

"% 4d: f:% 8e d:% 3.2e g:% 3.2e h:% 3.2e " | |

"rho:% 3.2e mu:% 3.2e eta:% 3.2e li:% 3d"; | |

string output = StringPrintf(kReportRowFormat, | |

summary.iteration, | |

summary.cost, | |

summary.cost_change, | |

summary.gradient_max_norm, | |

summary.step_norm, | |

summary.relative_decrease, | |

summary.trust_region_radius, | |

summary.eta, | |

summary.linear_solver_iterations); | |

if (log_to_stdout_) { | |

cout << output << endl; | |

} else { | |

VLOG(1) << output; | |

} | |

return SOLVER_CONTINUE; | |

} | |

private: | |

const bool log_to_stdout_; | |

}; | |

:class:`CRSMatrix` | |

================== | |

.. class:: CRSMatrix | |

A compressed row sparse matrix used primarily for communicating the | |

Jacobian matrix to the user. | |

.. member:: int CRSMatrix::num_rows | |

Number of rows. | |

.. member:: int CRSMatrix::num_cols | |

Number of columns. | |

.. member:: vector<int> CRSMatrix::rows | |

:member:`CRSMatrix::rows` is a :member:`CRSMatrix::num_rows` + 1 | |

sized array that points into the :member:`CRSMatrix::cols` and | |

:member:`CRSMatrix::values` array. | |

.. member:: vector<int> CRSMatrix::cols | |

:member:`CRSMatrix::cols` contain as many entries as there are | |

non-zeros in the matrix. | |

For each row ``i``, ``cols[rows[i]]`` ... ``cols[rows[i + 1] - 1]`` | |

are the indices of the non-zero columns of row ``i``. | |

.. member:: vector<int> CRSMatrix::values | |

:member:`CRSMatrix::values` contain as many entries as there are | |

non-zeros in the matrix. | |

For each row ``i``, | |

``values[rows[i]]`` ... ``values[rows[i + 1] - 1]`` are the values | |

of the non-zero columns of row ``i``. | |

e.g., consider the 3x4 sparse matrix | |

.. code-block:: c++ | |

0 10 0 4 | |

0 2 -3 2 | |

1 2 0 0 | |

The three arrays will be: | |

.. code-block:: c++ | |

-row0- ---row1--- -row2- | |

rows = [ 0, 2, 5, 7] | |

cols = [ 1, 3, 1, 2, 3, 0, 1] | |

values = [10, 4, 2, -3, 2, 1, 2] | |

:class:`Solver::Summary` | |

======================== | |

.. class:: Solver::Summary | |

Summary of the various stages of the solver after termination. | |

.. function:: string Solver::Summary::BriefReport() const | |

A brief one line description of the state of the solver after | |

termination. | |

.. function:: string Solver::Summary::FullReport() const | |

A full multiline description of the state of the solver after | |

termination. | |

.. function:: bool Solver::Summary::IsSolutionUsable() const | |

Whether the solution returned by the optimization algorithm can be | |

relied on to be numerically sane. This will be the case if | |

`Solver::Summary:termination_type` is set to `CONVERGENCE`, | |

`USER_SUCCESS` or `NO_CONVERGENCE`, i.e., either the solver | |

converged by meeting one of the convergence tolerances or because | |

the user indicated that it had converged or it ran to the maximum | |

number of iterations or time. | |

.. member:: MinimizerType Solver::Summary::minimizer_type | |

Type of minimization algorithm used. | |

.. member:: TerminationType Solver::Summary::termination_type | |

The cause of the minimizer terminating. | |

.. member:: string Solver::Summary::message | |

Reason why the solver terminated. | |

.. member:: double Solver::Summary::initial_cost | |

Cost of the problem (value of the objective function) before the | |

optimization. | |

.. member:: double Solver::Summary::final_cost | |

Cost of the problem (value of the objective function) after the | |

optimization. | |

.. member:: double Solver::Summary::fixed_cost | |

The part of the total cost that comes from residual blocks that | |

were held fixed by the preprocessor because all the parameter | |

blocks that they depend on were fixed. | |

.. member:: vector<IterationSummary> Solver::Summary::iterations | |

:class:`IterationSummary` for each minimizer iteration in order. | |

.. member:: int Solver::Summary::num_successful_steps | |

Number of minimizer iterations in which the step was | |

accepted. Unless :member:`Solver::Options::use_non_monotonic_steps` | |

is `true` this is also the number of steps in which the objective | |

function value/cost went down. | |

.. member:: int Solver::Summary::num_unsuccessful_steps | |

Number of minimizer iterations in which the step was rejected | |

either because it did not reduce the cost enough or the step was | |

not numerically valid. | |

.. member:: int Solver::Summary::num_inner_iteration_steps | |

Number of times inner iterations were performed. | |

.. member:: double Solver::Summary::preprocessor_time_in_seconds | |

Time (in seconds) spent in the preprocessor. | |

.. member:: double Solver::Summary::minimizer_time_in_seconds | |

Time (in seconds) spent in the Minimizer. | |

.. member:: double Solver::Summary::postprocessor_time_in_seconds | |

Time (in seconds) spent in the post processor. | |

.. member:: double Solver::Summary::total_time_in_seconds | |

Time (in seconds) spent in the solver. | |

.. member:: double Solver::Summary::linear_solver_time_in_seconds | |

Time (in seconds) spent in the linear solver computing the trust | |

region step. | |

.. member:: double Solver::Summary::residual_evaluation_time_in_seconds | |

Time (in seconds) spent evaluating the residual vector. | |

.. member:: double Solver::Summary::jacobian_evaluation_time_in_seconds | |

Time (in seconds) spent evaluating the Jacobian matrix. | |

.. member:: double Solver::Summary::inner_iteration_time_in_seconds | |

Time (in seconds) spent doing inner iterations. | |

.. member:: int Solver::Summary::num_parameter_blocks | |

Number of parameter blocks in the problem. | |

.. member:: int Solver::Summary::num_parameters | |

Number of parameters in the problem. | |

.. member:: int Solver::Summary::num_effective_parameters | |

Dimension of the tangent space of the problem (or the number of | |

columns in the Jacobian for the problem). This is different from | |

:member:`Solver::Summary::num_parameters` if a parameter block is | |

associated with a :class:`LocalParameterization`. | |

.. member:: int Solver::Summary::num_residual_blocks | |

Number of residual blocks in the problem. | |

.. member:: int Solver::Summary::num_residuals | |

Number of residuals in the problem. | |

.. member:: int Solver::Summary::num_parameter_blocks_reduced | |

Number of parameter blocks in the problem after the inactive and | |

constant parameter blocks have been removed. A parameter block is | |

inactive if no residual block refers to it. | |

.. member:: int Solver::Summary::num_parameters_reduced | |

Number of parameters in the reduced problem. | |

.. member:: int Solver::Summary::num_effective_parameters_reduced | |

Dimension of the tangent space of the reduced problem (or the | |

number of columns in the Jacobian for the reduced problem). This is | |

different from :member:`Solver::Summary::num_parameters_reduced` if | |

a parameter block in the reduced problem is associated with a | |

:class:`LocalParameterization`. | |

.. member:: int Solver::Summary::num_residual_blocks_reduced | |

Number of residual blocks in the reduced problem. | |

.. member:: int Solver::Summary::num_residuals_reduced | |

Number of residuals in the reduced problem. | |

.. member:: int Solver::Summary::num_threads_given | |

Number of threads specified by the user for Jacobian and residual | |

evaluation. | |

.. member:: int Solver::Summary::num_threads_used | |

Number of threads actually used by the solver for Jacobian and | |

residual evaluation. This number is not equal to | |

:member:`Solver::Summary::num_threads_given` if `OpenMP` is not | |

available. | |

.. member:: int Solver::Summary::num_linear_solver_threads_given | |

Number of threads specified by the user for solving the trust | |

region problem. | |

.. member:: int Solver::Summary::num_linear_solver_threads_used | |

Number of threads actually used by the solver for solving the trust | |

region problem. This number is not equal to | |

:member:`Solver::Summary::num_linear_solver_threads_given` if | |

`OpenMP` is not available. | |

.. member:: LinearSolverType Solver::Summary::linear_solver_type_given | |

Type of the linear solver requested by the user. | |

.. member:: LinearSolverType Solver::Summary::linear_solver_type_used | |

Type of the linear solver actually used. This may be different from | |

:member:`Solver::Summary::linear_solver_type_given` if Ceres | |

determines that the problem structure is not compatible with the | |

linear solver requested or if the linear solver requested by the | |

user is not available, e.g. The user requested | |

`SPARSE_NORMAL_CHOLESKY` but no sparse linear algebra library was | |

available. | |

.. member:: vector<int> Solver::Summary::linear_solver_ordering_given | |

Size of the elimination groups given by the user as hints to the | |

linear solver. | |

.. member:: vector<int> Solver::Summary::linear_solver_ordering_used | |

Size of the parameter groups used by the solver when ordering the | |

columns of the Jacobian. This maybe different from | |

:member:`Solver::Summary::linear_solver_ordering_given` if the user | |

left :member:`Solver::Summary::linear_solver_ordering_given` blank | |

and asked for an automatic ordering, or if the problem contains | |

some constant or inactive parameter blocks. | |

.. member:: bool Solver::Summary::inner_iterations_given | |

`True` if the user asked for inner iterations to be used as part of | |

the optimization. | |

.. member:: bool Solver::Summary::inner_iterations_used | |

`True` if the user asked for inner iterations to be used as part of | |

the optimization and the problem structure was such that they were | |

actually performed. For example, in a problem with just one parameter | |

block, inner iterations are not performed. | |

.. member:: vector<int> inner_iteration_ordering_given | |

Size of the parameter groups given by the user for performing inner | |

iterations. | |

.. member:: vector<int> inner_iteration_ordering_used | |

Size of the parameter groups given used by the solver for | |

performing inner iterations. This maybe different from | |

:member:`Solver::Summary::inner_iteration_ordering_given` if the | |

user left :member:`Solver::Summary::inner_iteration_ordering_given` | |

blank and asked for an automatic ordering, or if the problem | |

contains some constant or inactive parameter blocks. | |

.. member:: PreconditionerType Solver::Summary::preconditioner_type_given | |

Type of the preconditioner requested by the user. | |

.. member:: PreconditionerType Solver::Summary::preconditioner_type_used | |

Type of the preconditioner actually used. This may be different | |

from :member:`Solver::Summary::linear_solver_type_given` if Ceres | |

determines that the problem structure is not compatible with the | |

linear solver requested or if the linear solver requested by the | |

user is not available. | |

.. member:: VisibilityClusteringType Solver::Summary::visibility_clustering_type | |

Type of clustering algorithm used for visibility based | |

preconditioning. Only meaningful when the | |

:member:`Solver::Summary::preconditioner_type` is | |

``CLUSTER_JACOBI`` or ``CLUSTER_TRIDIAGONAL``. | |

.. member:: TrustRegionStrategyType Solver::Summary::trust_region_strategy_type | |

Type of trust region strategy. | |

.. member:: DoglegType Solver::Summary::dogleg_type | |

Type of dogleg strategy used for solving the trust region problem. | |

.. member:: DenseLinearAlgebraLibraryType Solver::Summary::dense_linear_algebra_library_type | |

Type of the dense linear algebra library used. | |

.. member:: SparseLinearAlgebraLibraryType Solver::Summary::sparse_linear_algebra_library_type | |

Type of the sparse linear algebra library used. | |

.. member:: LineSearchDirectionType Solver::Summary::line_search_direction_type | |

Type of line search direction used. | |

.. member:: LineSearchType Solver::Summary::line_search_type | |

Type of the line search algorithm used. | |

.. member:: LineSearchInterpolationType Solver::Summary::line_search_interpolation_type | |

When performing line search, the degree of the polynomial used to | |

approximate the objective function. | |

.. member:: NonlinearConjugateGradientType Solver::Summary::nonlinear_conjugate_gradient_type | |

If the line search direction is `NONLINEAR_CONJUGATE_GRADIENT`, | |

then this indicates the particular variant of non-linear conjugate | |

gradient used. | |

.. member:: int Solver::Summary::max_lbfgs_rank | |

If the type of the line search direction is `LBFGS`, then this | |

indicates the rank of the Hessian approximation. | |

Covariance Estimation | |

===================== | |

Background | |

---------- | |

One way to assess the quality of the solution returned by a | |

non-linear least squares solve is to analyze the covariance of the | |

solution. | |

Let us consider the non-linear regression problem | |

.. math:: y = f(x) + N(0, I) | |

i.e., the observation :math:`y` is a random non-linear function of the | |

independent variable :math:`x` with mean :math:`f(x)` and identity | |

covariance. Then the maximum likelihood estimate of :math:`x` given | |

observations :math:`y` is the solution to the non-linear least squares | |

problem: | |

.. math:: x^* = \arg \min_x \|f(x)\|^2 | |

And the covariance of :math:`x^*` is given by | |

.. math:: C(x^*) = \left(J'(x^*)J(x^*)\right)^{-1} | |

Here :math:`J(x^*)` is the Jacobian of :math:`f` at :math:`x^*`. The | |

above formula assumes that :math:`J(x^*)` has full column rank. | |

If :math:`J(x^*)` is rank deficient, then the covariance matrix :math:`C(x^*)` | |

is also rank deficient and is given by the Moore-Penrose pseudo inverse. | |

.. math:: C(x^*) = \left(J'(x^*)J(x^*)\right)^{\dagger} | |

Note that in the above, we assumed that the covariance matrix for | |

:math:`y` was identity. This is an important assumption. If this is | |

not the case and we have | |

.. math:: y = f(x) + N(0, S) | |

Where :math:`S` is a positive semi-definite matrix denoting the | |

covariance of :math:`y`, then the maximum likelihood problem to be | |

solved is | |

.. math:: x^* = \arg \min_x f'(x) S^{-1} f(x) | |

and the corresponding covariance estimate of :math:`x^*` is given by | |

.. math:: C(x^*) = \left(J'(x^*) S^{-1} J(x^*)\right)^{-1} | |

So, if it is the case that the observations being fitted to have a | |

covariance matrix not equal to identity, then it is the user's | |

responsibility that the corresponding cost functions are correctly | |

scaled, e.g. in the above case the cost function for this problem | |

should evaluate :math:`S^{-1/2} f(x)` instead of just :math:`f(x)`, | |

where :math:`S^{-1/2}` is the inverse square root of the covariance | |

matrix :math:`S`. | |

Gauge Invariance | |

---------------- | |

In structure from motion (3D reconstruction) problems, the | |

reconstruction is ambiguous upto a similarity transform. This is | |

known as a *Gauge Ambiguity*. Handling Gauges correctly requires the | |

use of SVD or custom inversion algorithms. For small problems the | |

user can use the dense algorithm. For more details see the work of | |

Kanatani & Morris [KanataniMorris]_. | |

:class:`Covariance` | |

------------------- | |

:class:`Covariance` allows the user to evaluate the covariance for a | |

non-linear least squares problem and provides random access to its | |

blocks. The computation assumes that the cost functions compute | |

residuals such that their covariance is identity. | |

Since the computation of the covariance matrix requires computing the | |

inverse of a potentially large matrix, this can involve a rather large | |

amount of time and memory. However, it is usually the case that the | |

user is only interested in a small part of the covariance | |

matrix. Quite often just the block diagonal. :class:`Covariance` | |

allows the user to specify the parts of the covariance matrix that she | |

is interested in and then uses this information to only compute and | |

store those parts of the covariance matrix. | |

Rank of the Jacobian | |

-------------------- | |

As we noted above, if the Jacobian is rank deficient, then the inverse | |

of :math:`J'J` is not defined and instead a pseudo inverse needs to be | |

computed. | |

The rank deficiency in :math:`J` can be *structural* -- columns | |

which are always known to be zero or *numerical* -- depending on the | |

exact values in the Jacobian. | |

Structural rank deficiency occurs when the problem contains parameter | |

blocks that are constant. This class correctly handles structural rank | |

deficiency like that. | |

Numerical rank deficiency, where the rank of the matrix cannot be | |

predicted by its sparsity structure and requires looking at its | |

numerical values is more complicated. Here again there are two | |

cases. | |

a. The rank deficiency arises from overparameterization. e.g., a | |

four dimensional quaternion used to parameterize :math:`SO(3)`, | |

which is a three dimensional manifold. In cases like this, the | |

user should use an appropriate | |

:class:`LocalParameterization`. Not only will this lead to better | |

numerical behaviour of the Solver, it will also expose the rank | |

deficiency to the :class:`Covariance` object so that it can | |

handle it correctly. | |

b. More general numerical rank deficiency in the Jacobian requires | |

the computation of the so called Singular Value Decomposition | |

(SVD) of :math:`J'J`. We do not know how to do this for large | |

sparse matrices efficiently. For small and moderate sized | |

problems this is done using dense linear algebra. | |

:class:`Covariance::Options` | |

.. class:: Covariance::Options | |

.. member:: int Covariance::Options::num_threads | |

Default: ``1`` | |

Number of threads to be used for evaluating the Jacobian and | |

estimation of covariance. | |

.. member:: CovarianceAlgorithmType Covariance::Options::algorithm_type | |

Default: ``SUITE_SPARSE_QR`` if ``SuiteSparseQR`` is installed and | |

``EIGEN_SPARSE_QR`` otherwise. | |

Ceres supports three different algorithms for covariance | |

estimation, which represent different tradeoffs in speed, accuracy | |

and reliability. | |

1. ``DENSE_SVD`` uses ``Eigen``'s ``JacobiSVD`` to perform the | |

computations. It computes the singular value decomposition | |

.. math:: U S V^\top = J | |

and then uses it to compute the pseudo inverse of J'J as | |

.. math:: (J'J)^{\dagger} = V S^{\dagger} V^\top | |

It is an accurate but slow method and should only be used for | |

small to moderate sized problems. It can handle full-rank as | |

well as rank deficient Jacobians. | |

2. ``EIGEN_SPARSE_QR`` uses the sparse QR factorization algorithm | |

in ``Eigen`` to compute the decomposition | |

.. math:: | |

QR &= J\\ | |

\left(J^\top J\right)^{-1} &= \left(R^\top R\right)^{-1} | |

It is a moderately fast algorithm for sparse matrices. | |

3. ``SUITE_SPARSE_QR`` uses the sparse QR factorization algorithm | |

in ``SuiteSparse``. It uses dense linear algebra and is multi | |

threaded, so for large sparse sparse matrices it is | |

significantly faster than ``EIGEN_SPARSE_QR``. | |

Neither ``EIGEN_SPARSE_QR`` nor ``SUITE_SPARSE_QR`` are capable of | |

computing the covariance if the Jacobian is rank deficient. | |

.. member:: int Covariance::Options::min_reciprocal_condition_number | |

Default: :math:`10^{-14}` | |

If the Jacobian matrix is near singular, then inverting :math:`J'J` | |

will result in unreliable results, e.g, if | |

.. math:: | |

J = \begin{bmatrix} | |

1.0& 1.0 \\ | |

1.0& 1.0000001 | |

\end{bmatrix} | |

which is essentially a rank deficient matrix, we have | |

.. math:: | |

(J'J)^{-1} = \begin{bmatrix} | |

2.0471e+14& -2.0471e+14 \\ | |

-2.0471e+14 2.0471e+14 | |

\end{bmatrix} | |

This is not a useful result. Therefore, by default | |

:func:`Covariance::Compute` will return ``false`` if a rank | |

deficient Jacobian is encountered. How rank deficiency is detected | |

depends on the algorithm being used. | |

1. ``DENSE_SVD`` | |

.. math:: \frac{\sigma_{\text{min}}}{\sigma_{\text{max}}} < \sqrt{\text{min_reciprocal_condition_number}} | |

where :math:`\sigma_{\text{min}}` and | |

:math:`\sigma_{\text{max}}` are the minimum and maxiumum | |

singular values of :math:`J` respectively. | |

2. ``EIGEN_SPARSE_QR`` and ``SUITE_SPARSE_QR`` | |

.. math:: \operatorname{rank}(J) < \operatorname{num\_col}(J) | |

Here :\math:`\operatorname{rank}(J)` is the estimate of the | |

rank of `J` returned by the sparse QR factorization | |

algorithm. It is a fairly reliable indication of rank | |

deficiency. | |

.. member:: int Covariance::Options::null_space_rank | |

When using ``DENSE_SVD``, the user has more control in dealing | |

with singular and near singular covariance matrices. | |

As mentioned above, when the covariance matrix is near singular, | |

instead of computing the inverse of :math:`J'J`, the Moore-Penrose | |

pseudoinverse of :math:`J'J` should be computed. | |

If :math:`J'J` has the eigen decomposition :math:`(\lambda_i, | |

e_i)`, where :math:`lambda_i` is the :math:`i^\textrm{th}` | |

eigenvalue and :math:`e_i` is the corresponding eigenvector, then | |

the inverse of :math:`J'J` is | |

.. math:: (J'J)^{-1} = \sum_i \frac{1}{\lambda_i} e_i e_i' | |

and computing the pseudo inverse involves dropping terms from this | |

sum that correspond to small eigenvalues. | |

How terms are dropped is controlled by | |

`min_reciprocal_condition_number` and `null_space_rank`. | |

If `null_space_rank` is non-negative, then the smallest | |

`null_space_rank` eigenvalue/eigenvectors are dropped irrespective | |

of the magnitude of :math:`\lambda_i`. If the ratio of the | |

smallest non-zero eigenvalue to the largest eigenvalue in the | |

truncated matrix is still below min_reciprocal_condition_number, | |

then the `Covariance::Compute()` will fail and return `false`. | |

Setting `null_space_rank = -1` drops all terms for which | |

.. math:: \frac{\lambda_i}{\lambda_{\textrm{max}}} < \textrm{min_reciprocal_condition_number} | |

This option has no effect on ``EIGEN_SPARSE_QR`` and | |

``SUITE_SPARSE_QR``. | |

.. member:: bool Covariance::Options::apply_loss_function | |

Default: `true` | |

Even though the residual blocks in the problem may contain loss | |

functions, setting ``apply_loss_function`` to false will turn off | |

the application of the loss function to the output of the cost | |

function and in turn its effect on the covariance. | |

.. class:: Covariance | |

:class:`Covariance::Options` as the name implies is used to control | |

the covariance estimation algorithm. Covariance estimation is a | |

complicated and numerically sensitive procedure. Please read the | |

entire documentation for :class:`Covariance::Options` before using | |

:class:`Covariance`. | |

.. function:: bool Covariance::Compute(const vector<pair<const double*, const double*> >& covariance_blocks, Problem* problem) | |

Compute a part of the covariance matrix. | |

The vector ``covariance_blocks``, indexes into the covariance | |

matrix block-wise using pairs of parameter blocks. This allows the | |

covariance estimation algorithm to only compute and store these | |

blocks. | |

Since the covariance matrix is symmetric, if the user passes | |

``<block1, block2>``, then ``GetCovarianceBlock`` can be called with | |

``block1``, ``block2`` as well as ``block2``, ``block1``. | |

``covariance_blocks`` cannot contain duplicates. Bad things will | |

happen if they do. | |

Note that the list of ``covariance_blocks`` is only used to | |

determine what parts of the covariance matrix are computed. The | |

full Jacobian is used to do the computation, i.e. they do not have | |

an impact on what part of the Jacobian is used for computation. | |

The return value indicates the success or failure of the covariance | |

computation. Please see the documentation for | |

:class:`Covariance::Options` for more on the conditions under which | |

this function returns ``false``. | |

.. function:: bool GetCovarianceBlock(const double* parameter_block1, const double* parameter_block2, double* covariance_block) const | |

Return the block of the covariance matrix corresponding to | |

``parameter_block1`` and ``parameter_block2``. | |

Compute must be called before the first call to ``GetCovarianceBlock`` | |

and the pair ``<parameter_block1, parameter_block2>`` OR the pair | |

``<parameter_block2, parameter_block1>`` must have been present in the | |

vector covariance_blocks when ``Compute`` was called. Otherwise | |

``GetCovarianceBlock`` will return false. | |

``covariance_block`` must point to a memory location that can store | |

a ``parameter_block1_size x parameter_block2_size`` matrix. The | |

returned covariance will be a row-major matrix. | |

Example Usage | |

------------- | |

.. code-block:: c++ | |

double x[3]; | |

double y[2]; | |

Problem problem; | |

problem.AddParameterBlock(x, 3); | |

problem.AddParameterBlock(y, 2); | |

<Build Problem> | |

<Solve Problem> | |

Covariance::Options options; | |

Covariance covariance(options); | |

vector<pair<const double*, const double*> > covariance_blocks; | |

covariance_blocks.push_back(make_pair(x, x)); | |

covariance_blocks.push_back(make_pair(y, y)); | |

covariance_blocks.push_back(make_pair(x, y)); | |

CHECK(covariance.Compute(covariance_blocks, &problem)); | |

double covariance_xx[3 * 3]; | |

double covariance_yy[2 * 2]; | |

double covariance_xy[3 * 2]; | |

covariance.GetCovarianceBlock(x, x, covariance_xx) | |

covariance.GetCovarianceBlock(y, y, covariance_yy) | |

covariance.GetCovarianceBlock(x, y, covariance_xy) |