clean up headers, add banners, refactor implementation to cpp

release/4.3a0
Duy-Nguyen Ta 2016-06-16 18:22:02 -04:00
parent 85b8fb5626
commit c55229673a
7 changed files with 181 additions and 92 deletions

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@ -1,3 +1,14 @@
/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file LP.h
* @brief Struct used to hold a Linear Programming Problem
@ -9,6 +20,7 @@
#include <gtsam_unstable/linear/LinearCost.h>
#include <gtsam_unstable/linear/EqualityFactorGraph.h>
#include <gtsam_unstable/linear/InequalityFactorGraph.h>
#include <string>

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@ -0,0 +1,109 @@
/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file LPInitSolver.h
* @brief This finds a feasible solution for an LP problem
* @author Duy Nguyen Ta
* @author Ivan Dario Jimenez
* @date 6/16/16
*/
#include <gtsam_unstable/linear/LPInitSolver.h>
#include <gtsam_unstable/linear/LPSolver.h>
namespace gtsam {
/******************************************************************************/
VectorValues LPInitSolver::solve() const {
// Build the graph to solve for the initial value of the initial problem
GaussianFactorGraph::shared_ptr initOfInitGraph = buildInitOfInitGraph();
VectorValues x0 = initOfInitGraph->optimize();
double y0 = compute_y0(x0);
Key yKey = maxKey(lp_) + 1; // the unique key for y0
VectorValues xy0(x0);
xy0.insert(yKey, Vector::Constant(1, y0));
// Formulate and solve the initial LP
LP::shared_ptr initLP = buildInitialLP(yKey);
// solve the initialLP
LPSolver lpSolveInit(*initLP);
VectorValues xyInit = lpSolveInit.optimize(xy0).first;
double yOpt = xyInit.at(yKey)[0];
xyInit.erase(yKey);
if (yOpt > 0)
throw InfeasibleOrUnboundedProblem();
else
return xyInit;
}
/******************************************************************************/
LP::shared_ptr LPInitSolver::buildInitialLP(Key yKey) const {
LP::shared_ptr initLP(new LP());
initLP->cost = LinearCost(yKey, I_1x1); // min y
initLP->equalities = lp_.equalities; // st. Ax = b
initLP->inequalities =
addSlackVariableToInequalities(yKey,
lp_.inequalities); // Cx-y <= d
return initLP;
}
/******************************************************************************/
GaussianFactorGraph::shared_ptr LPInitSolver::buildInitOfInitGraph() const {
// first add equality constraints Ax = b
GaussianFactorGraph::shared_ptr initGraph(
new GaussianFactorGraph(lp_.equalities));
// create factor ||x||^2 and add to the graph
const KeyDimMap& constrainedKeyDim = lp_.constrainedKeyDimMap();
for (Key key : constrainedKeyDim | boost::adaptors::map_keys) {
size_t dim = constrainedKeyDim.at(key);
initGraph->push_back(
JacobianFactor(key, Matrix::Identity(dim, dim), Vector::Zero(dim)));
}
return initGraph;
}
/******************************************************************************/
double LPInitSolver::compute_y0(const VectorValues& x0) const {
double y0 = -std::numeric_limits<double>::infinity();
for (const auto& factor : lp_.inequalities) {
double error = factor->error(x0);
if (error > y0) y0 = error;
}
return y0;
}
/******************************************************************************/
std::vector<std::pair<Key, Matrix> > LPInitSolver::collectTerms(
const LinearInequality& factor) const {
std::vector<std::pair<Key, Matrix> > terms;
for (Factor::const_iterator it = factor.begin(); it != factor.end(); it++) {
terms.push_back(make_pair(*it, factor.getA(it)));
}
return terms;
}
/******************************************************************************/
InequalityFactorGraph LPInitSolver::addSlackVariableToInequalities(
Key yKey, const InequalityFactorGraph& inequalities) const {
InequalityFactorGraph slackInequalities;
for (const auto& factor : lp_.inequalities) {
std::vector<std::pair<Key, Matrix> > terms = collectTerms(*factor); // Cx
terms.push_back(make_pair(yKey, Matrix::Constant(1, 1, -1.0))); // -y
double d = factor->getb()[0];
slackInequalities.push_back(LinearInequality(terms, d, factor->dualKey()));
}
return slackInequalities;
}
}

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/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file LPInitSolver.h
* @brief This LPInitSolver implements the strategy in Matlab.
@ -8,6 +19,7 @@
#pragma once
#include <gtsam_unstable/linear/LP.h>
#include <gtsam_unstable/linear/InfeasibleOrUnboundedProblem.h>
#include <gtsam_unstable/linear/QPSolver.h>
#include <CppUnitLite/Test.h>
@ -44,102 +56,35 @@ private:
const LP& lp_;
public:
LPInitSolver(const LP& lp) : lp_(lp) {
}
/// Construct with an LP problem
LPInitSolver(const LP& lp) : lp_(lp) {}
virtual ~LPInitSolver() {
}
virtual VectorValues solve() const {
// Build the graph to solve for the initial value of the initial problem
GaussianFactorGraph::shared_ptr initOfInitGraph = buildInitOfInitGraph();
VectorValues x0 = initOfInitGraph->optimize();
double y0 = compute_y0(x0);
Key yKey = maxKey(lp_) + 1; // the unique key for y0
VectorValues xy0(x0);
xy0.insert(yKey, Vector::Constant(1, y0));
// Formulate and solve the initial LP
LP::shared_ptr initLP = buildInitialLP(yKey);
// solve the initialLP
LPSolver lpSolveInit(*initLP);
VectorValues xyInit = lpSolveInit.optimize(xy0).first;
double yOpt = xyInit.at(yKey)[0];
xyInit.erase(yKey);
if (yOpt > 0)
throw InfeasibleOrUnboundedProblem();
else
return xyInit;
}
///@return a feasible initialization point
VectorValues solve() const;
private:
/// build initial LP
LP::shared_ptr buildInitialLP(Key yKey) const {
LP::shared_ptr initLP(new LP());
initLP->cost = LinearCost(yKey, I_1x1); // min y
initLP->equalities = lp_.equalities; // st. Ax = b
initLP->inequalities = addSlackVariableToInequalities(yKey,
lp_.inequalities); // Cx-y <= d
return initLP;
}
LP::shared_ptr buildInitialLP(Key yKey) const;
/**
* Build the following graph to solve for an initial value of the initial problem
* min ||x||^2 s.t. Ax = b
*/
GaussianFactorGraph::shared_ptr buildInitOfInitGraph() const {
// first add equality constraints Ax = b
GaussianFactorGraph::shared_ptr initGraph(
new GaussianFactorGraph(lp_.equalities));
// create factor ||x||^2 and add to the graph
const KeyDimMap& constrainedKeyDim = lp_.constrainedKeyDimMap();
for (Key key : constrainedKeyDim | boost::adaptors::map_keys) {
size_t dim = constrainedKeyDim.at(key);
initGraph->push_back(
JacobianFactor(key, Matrix::Identity(dim, dim), Vector::Zero(dim)));
}
return initGraph;
}
GaussianFactorGraph::shared_ptr buildInitOfInitGraph() const;
/// y = max_j ( Cj*x0 - dj ) -- due to the inequality constraints y >= Cx - d
double compute_y0(const VectorValues& x0) const {
double y0 = -std::numeric_limits<double>::infinity();
for (const auto& factor : lp_.inequalities) {
double error = factor->error(x0);
if (error > y0)
y0 = error;
}
return y0;
}
double compute_y0(const VectorValues& x0) const;
/// Collect all terms of a factor into a container.
std::vector<std::pair<Key, Matrix> > collectTerms(
const LinearInequality& factor) const {
std::vector < std::pair<Key, Matrix> > terms;
for (Factor::const_iterator it = factor.begin(); it != factor.end(); it++) {
terms.push_back(make_pair(*it, factor.getA(it)));
}
return terms;
}
const LinearInequality& factor) const;
/// Turn Cx <= d into Cx - y <= d factors
InequalityFactorGraph addSlackVariableToInequalities(Key yKey,
const InequalityFactorGraph& inequalities) const {
InequalityFactorGraph slackInequalities;
for (const auto& factor : lp_.inequalities) {
std::vector < std::pair<Key, Matrix> > terms = collectTerms(*factor); // Cx
terms.push_back(make_pair(yKey, Matrix::Constant(1, 1, -1.0))); // -y
double d = factor->getb()[0];
slackInequalities.push_back(
LinearInequality(terms, d, factor->dualKey()));
}
return slackInequalities;
}
const InequalityFactorGraph& inequalities) const;
// friend class for unit-testing private methods
FRIEND_TEST(LPInitSolver, initialization)
;
FRIEND_TEST(LPInitSolver, initialization);
};
}

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/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file LPSolver.cpp
* @brief
@ -7,9 +18,8 @@
*/
#include <gtsam_unstable/linear/LPSolver.h>
#include <gtsam_unstable/linear/InfeasibleInitialValues.h>
#include <gtsam/linear/GaussianFactorGraph.h>
#include <gtsam_unstable/linear/LPInitSolver.h>
#include <gtsam_unstable/linear/InfeasibleInitialValues.h>
namespace gtsam {
//******************************************************************************

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/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file LPSolver.h
* @brief Class used to solve Linear Programming Problems as defined in LP.h
@ -14,11 +25,8 @@
#include <gtsam_unstable/linear/LinearCost.h>
#include <gtsam/linear/VectorValues.h>
#include <boost/range/adaptor/map.hpp>
namespace gtsam {
class LPSolver: public ActiveSetSolver {
const LP &lp_; //!< the linear programming problem
public:

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/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file QPInitSolver.h
* @brief This QPInitSolver implements the strategy in Matlab.
* @brief This finds a feasible solution for a QP problem
* @author Duy Nguyen Ta
* @author Ivan Dario Jimenez
* @date 6/16/16
@ -24,9 +35,7 @@ public:
/// Constructor with a QP problem
QPInitSolver(const QP& qp) : qp_(qp) {}
/**
* @return a feasible initialization point
*/
///@return a feasible initialization point
VectorValues solve() const {
// Make an LP with any linear cost function. It doesn't matter for
// initialization.

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@ -16,13 +16,9 @@
* @author Duy-Nguyen Ta
*/
#include <gtsam/inference/Symbol.h>
#include <gtsam/inference/FactorGraph-inst.h>
#include <gtsam_unstable/linear/QPSolver.h>
#include <gtsam_unstable/linear/LPSolver.h>
#include <gtsam_unstable/linear/InfeasibleInitialValues.h>
#include <gtsam_unstable/linear/QPInitSolver.h>
#include <boost/range/adaptor/map.hpp>
#include <gtsam_unstable/linear/InfeasibleInitialValues.h>
using namespace std;