diff --git a/include/ceres/solver.h b/include/ceres/solver.h
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+// Ceres Solver - A fast non-linear least squares minimizer
+// Copyright 2010, 2011, 2012 Google Inc. All rights reserved.
+// http://code.google.com/p/ceres-solver/
+//
+// Redistribution and use in source and binary forms, with or without
+// modification, are permitted provided that the following conditions are met:
+//
+// * Redistributions of source code must retain the above copyright notice,
+//   this list of conditions and the following disclaimer.
+// * Redistributions in binary form must reproduce the above copyright notice,
+//   this list of conditions and the following disclaimer in the documentation
+//   and/or other materials provided with the distribution.
+// * Neither the name of Google Inc. nor the names of its contributors may be
+//   used to endorse or promote products derived from this software without
+//   specific prior written permission.
+//
+// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+// POSSIBILITY OF SUCH DAMAGE.
+//
+// Author: sameeragarwal@google.com (Sameer Agarwal)
+
+#ifndef CERES_PUBLIC_SOLVER_H_
+#define CERES_PUBLIC_SOLVER_H_
+
+#include <cmath>
+#include <string>
+#include <vector>
+
+#include "ceres/iteration_callback.h"
+#include "ceres/internal/macros.h"
+#include "ceres/internal/port.h"
+#include "ceres/types.h"
+
+namespace ceres {
+
+class Problem;
+
+// Interface for non-linear least squares solvers.
+class Solver {
+ public:
+  virtual ~Solver();
+
+  // The options structure contains, not surprisingly, options that control how
+  // the solver operates. The defaults should be suitable for a wide range of
+  // problems; however, better performance is often obtainable with tweaking.
+  //
+  // The constants are defined inside types.h
+  struct Options {
+    // Default constructor that sets up a generic sparse problem.
+    Options() {
+      minimizer_type = LEVENBERG_MARQUARDT;
+      max_num_iterations = 50;
+      max_solver_time_sec = 1.0e9;
+      num_threads = 1;
+      tau = 1e-4;
+      min_relative_decrease = 1e-3;
+      function_tolerance = 1e-6;
+      gradient_tolerance = 1e-10;
+      parameter_tolerance = 1e-8;
+#ifndef CERES_NO_SUITESPARSE
+      linear_solver_type = SPARSE_NORMAL_CHOLESKY;
+#else
+      linear_solver_type = DENSE_QR;
+#endif  // CERES_NO_SUITESPARSE
+      preconditioner_type = JACOBI;
+      num_linear_solver_threads = 1;
+      num_eliminate_blocks = 0;
+      ordering_type = NATURAL;
+      linear_solver_min_num_iterations = 1;
+      linear_solver_max_num_iterations = 500;
+      eta = 1e-1;
+      jacobi_scaling = true;
+      logging_type = PER_MINIMIZER_ITERATION;
+      minimizer_progress_to_stdout = false;
+      return_initial_residuals = false;
+      return_final_residuals = false;
+      lsqp_dump_format = "lm_iteration_%03d.lsqp";
+      crash_and_dump_lsqp_on_failure = false;
+      check_gradients = false;
+      gradient_check_relative_precision = 1e-8;
+      numeric_derivative_relative_step_size = 1e-6;
+      update_state_every_iteration = false;
+    }
+
+    // Minimizer options ----------------------------------------
+
+    MinimizerType minimizer_type;
+
+    // Maximum number of iterations for the minimizer to run for.
+    int max_num_iterations;
+
+    // Maximum time for which the minimizer should run for.
+    double max_solver_time_sec;
+
+    // Number of threads used by Ceres for evaluating the cost and
+    // jacobians.
+    int num_threads;
+
+    // For Levenberg-Marquardt, the initial value for the
+    // regularizer. This is the inversely related to the size of the
+    // initial trust region.
+    double tau;
+
+    // For trust region methods, this is lower threshold for the
+    // relative decrease before a step is accepted.
+    double min_relative_decrease;
+
+    // Minimizer terminates when
+    //
+    //   (new_cost - old_cost) < function_tolerance * old_cost;
+    //
+    double function_tolerance;
+
+    // Minimizer terminates when
+    //
+    //   max_i |gradient_i| < gradient_tolerance * max_i|initial_gradient_i|
+    //
+    // This value should typically be 1e-4 * function_tolerance.
+    double gradient_tolerance;
+
+    // Minimizer terminates when
+    //
+    //   |step|_2 <= parameter_tolerance * ( |x|_2 +  parameter_tolerance)
+    //
+    double parameter_tolerance;
+
+    // Linear least squares solver options -------------------------------------
+
+    LinearSolverType linear_solver_type;
+
+    // Type of preconditioner to use with the iterative linear solvers.
+    PreconditionerType preconditioner_type;
+
+    // Number of threads used by Ceres to solve the Newton
+    // step. Currently only the SPARSE_SCHUR solver is capable of
+    // using this setting.
+    int num_linear_solver_threads;
+
+    // For Schur reduction based methods, the first 0 to num blocks are
+    // eliminated using the Schur reduction. For example, when solving
+    // traditional structure from motion problems where the parameters are in
+    // two classes (cameras and points) then num_eliminate_blocks would be the
+    // number of points.
+    //
+    // This parameter is used in conjunction with the ordering.
+    // Applies to: Preprocessor and linear least squares solver.
+    int num_eliminate_blocks;
+
+    // Internally Ceres reorders the parameter blocks to help the
+    // various linear solvers. This parameter allows the user to
+    // influence the re-ordering strategy used. For structure from
+    // motion problems use SCHUR, for other problems NATURAL (default)
+    // is a good choice. In case you wish to specify your own ordering
+    // scheme, for example in conjunction with num_eliminate_blocks,
+    // use USER.
+    OrderingType ordering_type;
+
+    // The ordering of the parameter blocks. The solver pays attention
+    // to it if the ordering_type is set to USER and the vector is
+    // non-empty.
+    vector<double*> ordering;
+
+
+    // Minimum number of iterations for which the linear solver should
+    // run, even if the convergence criterion is satisfied.
+    int linear_solver_min_num_iterations;
+
+    // Maximum number of iterations for which the linear solver should
+    // run. If the solver does not converge in less than
+    // linear_solver_max_num_iterations, then it returns
+    // MAX_ITERATIONS, as its termination type.
+    int linear_solver_max_num_iterations;
+
+    // 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
+    //
+    //  (Q_i - Q_{i-1})/Q_i < eta/i
+    double eta;
+
+    // Normalize the jacobian using Jacobi scaling before calling
+    // the linear least squares solver.
+    bool jacobi_scaling;
+
+    // Logging options ---------------------------------------------------------
+
+    LoggingType logging_type;
+
+    // By default the Minimizer progress is logged to VLOG(1), which
+    // is sent to STDERR depending on the vlog level. If this flag is
+    // set to true, and logging_type is not SILENT, the logging output
+    // is sent to STDOUT.
+    bool minimizer_progress_to_stdout;
+
+    bool return_initial_residuals;
+    bool return_final_residuals;
+
+    // List of iterations at which the optimizer should dump the
+    // linear least squares problem to disk. Useful for testing and
+    // benchmarking. If empty (default), no problems are dumped.
+    //
+    // This is ignored if protocol buffers are disabled.
+    vector<int> lsqp_iterations_to_dump;
+
+    // Format string for the file name used for dumping the least
+    // squares problem to disk. If the format is 'ascii', then the
+    // problem is logged to the screen; don't try this with large
+    // problems or expect a frozen terminal.
+    string lsqp_dump_format;
+
+    // Dump the linear least squares problem to disk if the minimizer
+    // fails due to NUMERICAL_FAILURE and crash the process. This flag
+    // is useful for generating debugging information. The problem is
+    // dumped in a file whose name is determined by
+    // Solver::Options::lsqp_dump_format.
+    //
+    // Note: This requires a version of Ceres built with protocol buffers.
+    bool crash_and_dump_lsqp_on_failure;
+
+    // Finite differences options ----------------------------------------------
+
+    // 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.
+    bool check_gradients;
+
+    // Relative 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.
+    double gradient_check_relative_precision;
+
+    // Relative shift used for taking numeric derivatives. For finite
+    // differencing, each dimension is evaluated at slightly shifted
+    // values; for the case of central difference, this is what gets
+    // evaluated:
+    //
+    //   delta = numeric_derivative_relative_step_size;
+    //   f_initial  = f(x)
+    //   f_forward  = f((1 + delta) * x)
+    //   f_backward = f((1 - 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 differentation works for functions where
+    // the arguments are typically large (e.g. 1e9) and when the
+    // values are small (e.g. 1e-5). It is possible to construct
+    // "torture cases" which break this finite difference heuristic,
+    // but they do not come up often in practice.
+    //
+    // TODO(keir): Pick a smarter number than the default above! In
+    // theory a good choice is sqrt(eps) * x, which for doubles means
+    // about 1e-8 * x. However, I have found this number too
+    // optimistic. This number should be exposed for users to change.
+    double numeric_derivative_relative_step_size;
+
+    // If true, the user's parameter blocks are updated at the end of
+    // every Minimizer iteration, otherwise they are updated when the
+    // Minimizer terminates. This is useful if, for example, the user
+    // wishes to visualize the state of the optimization every
+    // iteration.
+    bool update_state_every_iteration;
+
+    // Callbacks that are executed at the end of each iteration of the
+    // 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
+    // Minimizer terminates. This behaviour is controlled by
+    // update_state_every_variable. If the user wishes to have access
+    // to the update parameter blocks when his/her callbacks are
+    // executed, then set update_state_every_iteration to true.
+    //
+    // The solver does NOT take ownership of these pointers.
+    vector<IterationCallback*> callbacks;
+  };
+
+  struct Summary {
+    Summary();
+
+    // A brief one line description of the state of the solver after
+    // termination.
+    string BriefReport() const;
+
+    // A full multiline description of the state of the solver after
+    // termination.
+    string FullReport() const;
+
+    // Minimizer summary -------------------------------------------------
+    SolverTerminationType termination_type;
+
+    // If the solver did not run, or there was a failure, a
+    // description of the error.
+    string error;
+
+    // Cost of the problem before and after the optimization. See
+    // problem.h for definition of the cost of a problem.
+    double initial_cost;
+    double final_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.
+    double fixed_cost;
+
+    // Residuals before and after the optimization. Each vector
+    // contains problem.NumResiduals() elements. Residuals are in the
+    // same order in which they were added to the problem object when
+    // constructing this problem.
+    vector<double> initial_residuals;
+    vector<double> final_residuals;
+
+    vector<IterationSummary> iterations;
+
+    int num_successful_steps;
+    int num_unsuccessful_steps;
+
+    double preprocessor_time_in_seconds;
+    double minimizer_time_in_seconds;
+    double total_time_in_seconds;
+
+    // Preprocessor summary.
+    int num_parameter_blocks;
+    int num_parameters;
+    int num_residual_blocks;
+    int num_residuals;
+
+    int num_parameter_blocks_reduced;
+    int num_parameters_reduced;
+    int num_residual_blocks_reduced;
+    int num_residuals_reduced;
+
+    int num_eliminate_blocks_given;
+    int num_eliminate_blocks_used;
+
+    int num_threads_given;
+    int num_threads_used;
+
+    int num_linear_solver_threads_given;
+    int num_linear_solver_threads_used;
+
+    LinearSolverType linear_solver_type_given;
+    LinearSolverType linear_solver_type_used;
+
+    PreconditionerType preconditioner_type;
+    OrderingType ordering_type;
+  };
+
+  // Once a least squares problem has been built, this function takes
+  // the problem and optimizes it based on the values of the options
+  // parameters. Upon return, a detailed summary of the work performed
+  // by the preprocessor, the non-linear minmizer and the linear
+  // solver are reported in the summary object.
+  virtual void Solve(const Options& options,
+                     Problem* problem,
+                     Solver::Summary* summary);
+};
+
+// Helper function which avoids going through the interface.
+void Solve(const Solver::Options& options,
+           Problem* problem,
+           Solver::Summary* summary);
+
+}  // namespace ceres
+
+#endif  // CERES_PUBLIC_SOLVER_H_