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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2015 Google Inc. All rights reserved.
// http://ceres-solver.org/
//
// 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.
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// used to endorse or promote products derived from this software without
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//
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// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// POSSIBILITY OF SUCH DAMAGE.
//
// Author: sameeragarwal@google.com (Sameer Agarwal)
//
// Various algorithms that operate on undirected graphs.
#ifndef CERES_INTERNAL_GRAPH_ALGORITHMS_H_
#define CERES_INTERNAL_GRAPH_ALGORITHMS_H_
#include <algorithm>
#include <memory>
#include <unordered_map>
#include <unordered_set>
#include <utility>
#include <vector>
#include "ceres/graph.h"
#include "ceres/internal/export.h"
#include "ceres/wall_time.h"
#include "glog/logging.h"
namespace ceres::internal {
// Compare two vertices of a graph by their degrees, if the degrees
// are equal then order them by their ids.
template <typename Vertex>
class CERES_NO_EXPORT VertexTotalOrdering {
public:
explicit VertexTotalOrdering(const Graph<Vertex>& graph) : graph_(graph) {}
bool operator()(const Vertex& lhs, const Vertex& rhs) const {
if (graph_.Neighbors(lhs).size() == graph_.Neighbors(rhs).size()) {
return lhs < rhs;
}
return graph_.Neighbors(lhs).size() < graph_.Neighbors(rhs).size();
}
private:
const Graph<Vertex>& graph_;
};
template <typename Vertex>
class VertexDegreeLessThan {
public:
explicit VertexDegreeLessThan(const Graph<Vertex>& graph) : graph_(graph) {}
bool operator()(const Vertex& lhs, const Vertex& rhs) const {
return graph_.Neighbors(lhs).size() < graph_.Neighbors(rhs).size();
}
private:
const Graph<Vertex>& graph_;
};
// Order the vertices of a graph using its (approximately) largest
// independent set, where an independent set of a graph is a set of
// vertices that have no edges connecting them. The maximum
// independent set problem is NP-Hard, but there are effective
// approximation algorithms available. The implementation here uses a
// breadth first search that explores the vertices in order of
// increasing degree. The same idea is used by Saad & Li in "MIQR: A
// multilevel incomplete QR preconditioner for large sparse
// least-squares problems", SIMAX, 2007.
//
// Given a undirected graph G(V,E), the algorithm is a greedy BFS
// search where the vertices are explored in increasing order of their
// degree. The output vector ordering contains elements of S in
// increasing order of their degree, followed by elements of V - S in
// increasing order of degree. The return value of the function is the
// cardinality of S.
template <typename Vertex>
int IndependentSetOrdering(const Graph<Vertex>& graph,
std::vector<Vertex>* ordering) {
const std::unordered_set<Vertex>& vertices = graph.vertices();
const int num_vertices = vertices.size();
CHECK(ordering != nullptr);
ordering->clear();
ordering->reserve(num_vertices);
// Colors for labeling the graph during the BFS.
const char kWhite = 0;
const char kGrey = 1;
const char kBlack = 2;
// Mark all vertices white.
std::unordered_map<Vertex, char> vertex_color;
std::vector<Vertex> vertex_queue;
for (const Vertex& vertex : vertices) {
vertex_color[vertex] = kWhite;
vertex_queue.push_back(vertex);
}
std::sort(vertex_queue.begin(),
vertex_queue.end(),
VertexTotalOrdering<Vertex>(graph));
// Iterate over vertex_queue. Pick the first white vertex, add it
// to the independent set. Mark it black and its neighbors grey.
for (const Vertex& vertex : vertex_queue) {
if (vertex_color[vertex] != kWhite) {
continue;
}
ordering->push_back(vertex);
vertex_color[vertex] = kBlack;
const std::unordered_set<Vertex>& neighbors = graph.Neighbors(vertex);
for (const Vertex& neighbor : neighbors) {
vertex_color[neighbor] = kGrey;
}
}
int independent_set_size = ordering->size();
// Iterate over the vertices and add all the grey vertices to the
// ordering. At this stage there should only be black or grey
// vertices in the graph.
for (const Vertex& vertex : vertex_queue) {
DCHECK(vertex_color[vertex] != kWhite);
if (vertex_color[vertex] != kBlack) {
ordering->push_back(vertex);
}
}
CHECK_EQ(ordering->size(), num_vertices);
return independent_set_size;
}
// Same as above with one important difference. The ordering parameter
// is an input/output parameter which carries an initial ordering of
// the vertices of the graph. The greedy independent set algorithm
// starts by sorting the vertices in increasing order of their
// degree. The input ordering is used to stabilize this sort, i.e., if
// two vertices have the same degree then they are ordered in the same
// order in which they occur in "ordering".
//
// This is useful in eliminating non-determinism from the Schur
// ordering algorithm over all.
template <typename Vertex>
int StableIndependentSetOrdering(const Graph<Vertex>& graph,
std::vector<Vertex>* ordering) {
CHECK(ordering != nullptr);
const std::unordered_set<Vertex>& vertices = graph.vertices();
const int num_vertices = vertices.size();
CHECK_EQ(vertices.size(), ordering->size());
// Colors for labeling the graph during the BFS.
const char kWhite = 0;
const char kGrey = 1;
const char kBlack = 2;
std::vector<Vertex> vertex_queue(*ordering);
std::stable_sort(vertex_queue.begin(),
vertex_queue.end(),
VertexDegreeLessThan<Vertex>(graph));
// Mark all vertices white.
std::unordered_map<Vertex, char> vertex_color;
for (const Vertex& vertex : vertices) {
vertex_color[vertex] = kWhite;
}
ordering->clear();
ordering->reserve(num_vertices);
// Iterate over vertex_queue. Pick the first white vertex, add it
// to the independent set. Mark it black and its neighbors grey.
for (int i = 0; i < vertex_queue.size(); ++i) {
const Vertex& vertex = vertex_queue[i];
if (vertex_color[vertex] != kWhite) {
continue;
}
ordering->push_back(vertex);
vertex_color[vertex] = kBlack;
const std::unordered_set<Vertex>& neighbors = graph.Neighbors(vertex);
for (const Vertex& neighbor : neighbors) {
vertex_color[neighbor] = kGrey;
}
}
int independent_set_size = ordering->size();
// Iterate over the vertices and add all the grey vertices to the
// ordering. At this stage there should only be black or grey
// vertices in the graph.
for (const Vertex& vertex : vertex_queue) {
DCHECK(vertex_color[vertex] != kWhite);
if (vertex_color[vertex] != kBlack) {
ordering->push_back(vertex);
}
}
CHECK_EQ(ordering->size(), num_vertices);
return independent_set_size;
}
// Find the connected component for a vertex implemented using the
// find and update operation for disjoint-set. Recursively traverse
// the disjoint set structure till you reach a vertex whose connected
// component has the same id as the vertex itself. Along the way
// update the connected components of all the vertices. This updating
// is what gives this data structure its efficiency.
template <typename Vertex>
Vertex FindConnectedComponent(const Vertex& vertex,
std::unordered_map<Vertex, Vertex>* union_find) {
auto it = union_find->find(vertex);
DCHECK(it != union_find->end());
if (it->second != vertex) {
it->second = FindConnectedComponent(it->second, union_find);
}
return it->second;
}
// Compute a degree two constrained Maximum Spanning Tree/forest of
// the input graph. Caller owns the result.
//
// Finding degree 2 spanning tree of a graph is not always
// possible. For example a star graph, i.e. a graph with n-nodes
// where one node is connected to the other n-1 nodes does not have
// a any spanning trees of degree less than n-1.Even if such a tree
// exists, finding such a tree is NP-Hard.
// We get around both of these problems by using a greedy, degree
// constrained variant of Kruskal's algorithm. We start with a graph
// G_T with the same vertex set V as the input graph G(V,E) but an
// empty edge set. We then iterate over the edges of G in decreasing
// order of weight, adding them to G_T if doing so does not create a
// cycle in G_T} and the degree of all the vertices in G_T remains
// bounded by two. This O(|E|) algorithm results in a degree-2
// spanning forest, or a collection of linear paths that span the
// graph G.
template <typename Vertex>
std::unique_ptr<WeightedGraph<Vertex>> Degree2MaximumSpanningForest(
const WeightedGraph<Vertex>& graph) {
// Array of edges sorted in decreasing order of their weights.
std::vector<std::pair<double, std::pair<Vertex, Vertex>>> weighted_edges;
auto forest = std::make_unique<WeightedGraph<Vertex>>();
// Disjoint-set to keep track of the connected components in the
// maximum spanning tree.
std::unordered_map<Vertex, Vertex> disjoint_set;
// Sort of the edges in the graph in decreasing order of their
// weight. Also add the vertices of the graph to the Maximum
// Spanning Tree graph and set each vertex to be its own connected
// component in the disjoint_set structure.
const std::unordered_set<Vertex>& vertices = graph.vertices();
for (const Vertex& vertex1 : vertices) {
forest->AddVertex(vertex1, graph.VertexWeight(vertex1));
disjoint_set[vertex1] = vertex1;
const std::unordered_set<Vertex>& neighbors = graph.Neighbors(vertex1);
for (const Vertex& vertex2 : neighbors) {
if (vertex1 >= vertex2) {
continue;
}
const double weight = graph.EdgeWeight(vertex1, vertex2);
weighted_edges.push_back(
std::make_pair(weight, std::make_pair(vertex1, vertex2)));
}
}
// The elements of this vector, are pairs<edge_weight,
// edge>. Sorting it using the reverse iterators gives us the edges
// in decreasing order of edges.
std::sort(weighted_edges.rbegin(), weighted_edges.rend());
// Greedily add edges to the spanning tree/forest as long as they do
// not violate the degree/cycle constraint.
for (int i = 0; i < weighted_edges.size(); ++i) {
const std::pair<Vertex, Vertex>& edge = weighted_edges[i].second;
const Vertex vertex1 = edge.first;
const Vertex vertex2 = edge.second;
// Check if either of the vertices are of degree 2 already, in
// which case adding this edge will violate the degree 2
// constraint.
if ((forest->Neighbors(vertex1).size() == 2) ||
(forest->Neighbors(vertex2).size() == 2)) {
continue;
}
// Find the id of the connected component to which the two
// vertices belong to. If the id is the same, it means that the
// two of them are already connected to each other via some other
// vertex, and adding this edge will create a cycle.
Vertex root1 = FindConnectedComponent(vertex1, &disjoint_set);
Vertex root2 = FindConnectedComponent(vertex2, &disjoint_set);
if (root1 == root2) {
continue;
}
// This edge can be added, add an edge in either direction with
// the same weight as the original graph.
const double edge_weight = graph.EdgeWeight(vertex1, vertex2);
forest->AddEdge(vertex1, vertex2, edge_weight);
forest->AddEdge(vertex2, vertex1, edge_weight);
// Connected the two connected components by updating the
// disjoint_set structure. Always connect the connected component
// with the greater index with the connected component with the
// smaller index. This should ensure shallower trees, for quicker
// lookup.
if (root2 < root1) {
std::swap(root1, root2);
}
disjoint_set[root2] = root1;
}
return forest;
}
} // namespace ceres::internal
#endif // CERES_INTERNAL_GRAPH_ALGORITHMS_H_