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finish ActiveSetSolver

release/4.3a0
Duy-Nguyen Ta 2016-06-16 23:49:14 -04:00
parent c55229673a
commit a2ca05fb8a
14 changed files with 514 additions and 732 deletions
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290
gtsam_unstable/linear/ActiveSetSolver-inl.h Normal file
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@ -0,0 +1,290 @@
/* ----------------------------------------------------------------------------
* 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 ActiveSetSolver-inl.h
* @brief Implmentation of ActiveSetSolver.
* @author Ivan Dario Jimenez
* @author Duy Nguyen Ta
* @date 2/11/16
*/
#include <gtsam_unstable/linear/InfeasibleInitialValues.h>
/******************************************************************************/
// Convenient macros to reduce syntactic noise. undef later.
#define Template template <class PROBLEM, class POLICY, class INITSOLVER>
#define This ActiveSetSolver<PROBLEM, POLICY, INITSOLVER>
/******************************************************************************/
namespace gtsam {
/* We have to make sure the new solution with alpha satisfies all INACTIVE inequality constraints
* If some inactive inequality constraints complain about the full step (alpha = 1),
* we have to adjust alpha to stay within the inequality constraints' feasible regions.
*
* For each inactive inequality j:
* - We already have: aj'*xk - bj <= 0, since xk satisfies all inequality constraints
* - We want: aj'*(xk + alpha*p) - bj <= 0
* - If aj'*p <= 0, we have: aj'*(xk + alpha*p) <= aj'*xk <= bj, for all alpha>0
* it's good!
* - We only care when aj'*p > 0. In this case, we need to choose alpha so that
* aj'*xk + alpha*aj'*p - bj <= 0 --> alpha <= (bj - aj'*xk) / (aj'*p)
* We want to step as far as possible, so we should choose alpha = (bj - aj'*xk) / (aj'*p)
*
* We want the minimum of all those alphas among all inactive inequality.
*/
Template boost::tuple<double, int> This::computeStepSize(
const InequalityFactorGraph& workingSet, const VectorValues& xk,
const VectorValues& p, const double& maxAlpha) const {
double minAlpha = maxAlpha;
int closestFactorIx = -1;
for (size_t factorIx = 0; factorIx < workingSet.size(); ++factorIx) {
const LinearInequality::shared_ptr& factor = workingSet.at(factorIx);
double b = factor->getb()[0];
// only check inactive factors
if (!factor->active()) {
// Compute a'*p
double aTp = factor->dotProductRow(p);
// Check if a'*p >0. Don't care if it's not.
if (aTp <= 0)
continue;
// Compute a'*xk
double aTx = factor->dotProductRow(xk);
// alpha = (b - a'*xk) / (a'*p)
double alpha = (b - aTx) / aTp;
// We want the minimum of all those max alphas
if (alpha < minAlpha) {
closestFactorIx = factorIx;
minAlpha = alpha;
}
}
}
return boost::make_tuple(minAlpha, closestFactorIx);
}
/******************************************************************************/
/*
* The goal of this function is to find currently active inequality constraints
* that violate the condition to be active. The one that violates the condition
* the most will be removed from the active set. See Nocedal06book, pg 469-471
*
* Find the BAD active inequality that pulls x strongest to the wrong direction
* of its constraint (i.e. it is pulling towards >0, while its feasible region is <=0)
*
* For active inequality constraints (those that are enforced as equality constraints
* in the current working set), we want lambda < 0.
* This is because:
* - From the Lagrangian L = f - lambda*c, we know that the constraint force
* is (lambda * \grad c) = \grad f. Intuitively, to keep the solution x stay
* on the constraint surface, the constraint force has to balance out with
* other unconstrained forces that are pulling x towards the unconstrained
* minimum point. The other unconstrained forces are pulling x toward (-\grad f),
* hence the constraint force has to be exactly \grad f, so that the total
* force is 0.
* - We also know that at the constraint surface c(x)=0, \grad c points towards + (>= 0),
* while we are solving for - (<=0) constraint.
* - We want the constraint force (lambda * \grad c) to pull x towards the - (<=0) direction
* i.e., the opposite direction of \grad c where the inequality constraint <=0 is satisfied.
* That means we want lambda < 0.
* - This is because when the constrained force pulls x towards the infeasible region (+),
* the unconstrained force is pulling x towards the opposite direction into
* the feasible region (again because the total force has to be 0 to make x stay still)
* So we can drop this constraint to have a lower error but feasible solution.
*
* In short, active inequality constraints with lambda > 0 are BAD, because they
* violate the condition to be active.
*
* And we want to remove the worst one with the largest lambda from the active set.
*
*/
Template int This::identifyLeavingConstraint(
const InequalityFactorGraph& workingSet,
const VectorValues& lambdas) const {
int worstFactorIx = -1;
// preset the maxLambda to 0.0: if lambda is <= 0.0, the constraint is either
// inactive or a good inequality constraint, so we don't care!
double maxLambda = 0.0;
for (size_t factorIx = 0; factorIx < workingSet.size(); ++factorIx) {
const LinearInequality::shared_ptr& factor = workingSet.at(factorIx);
if (factor->active()) {
double lambda = lambdas.at(factor->dualKey())[0];
if (lambda > maxLambda) {
worstFactorIx = factorIx;
maxLambda = lambda;
}
}
}
return worstFactorIx;
}
//******************************************************************************
Template JacobianFactor::shared_ptr This::createDualFactor(
Key key, const InequalityFactorGraph& workingSet,
const VectorValues& delta) const {
// Transpose the A matrix of constrained factors to have the jacobian of the
// dual key
TermsContainer Aterms = collectDualJacobians<LinearEquality>(
key, problem_.equalities, equalityVariableIndex_);
TermsContainer AtermsInequalities = collectDualJacobians<LinearInequality>(
key, workingSet, inequalityVariableIndex_);
Aterms.insert(Aterms.end(), AtermsInequalities.begin(),
AtermsInequalities.end());
// Collect the gradients of unconstrained cost factors to the b vector
if (Aterms.size() > 0) {
Vector b = problem_.costGradient(key, delta);
// to compute the least-square approximation of dual variables
return boost::make_shared<JacobianFactor>(Aterms, b);
} else {
return boost::make_shared<JacobianFactor>();
}
}
/******************************************************************************/
/* This function will create a dual graph that solves for the
* lagrange multipliers for the current working set.
* You can use lagrange multipliers as a necessary condition for optimality.
* The factor graph that is being solved is f' = -lambda * g'
* where f is the optimized function and g is the function resulting from
* aggregating the working set.
* The lambdas give you information about the feasibility of a constraint.
* if lambda < 0 the constraint is Ok
* if lambda = 0 you are on the constraint
* if lambda > 0 you are violating the constraint.
*/
Template GaussianFactorGraph::shared_ptr This::buildDualGraph(
const InequalityFactorGraph& workingSet, const VectorValues& delta) const {
GaussianFactorGraph::shared_ptr dualGraph(new GaussianFactorGraph());
for (Key key : constrainedKeys_) {
// Each constrained key becomes a factor in the dual graph
JacobianFactor::shared_ptr dualFactor =
createDualFactor(key, workingSet, delta);
if (!dualFactor->empty()) dualGraph->push_back(dualFactor);
}
return dualGraph;
}
//******************************************************************************
Template GaussianFactorGraph
This::buildWorkingGraph(const InequalityFactorGraph& workingSet,
const VectorValues& xk) const {
GaussianFactorGraph workingGraph;
workingGraph.push_back(POLICY::buildCostFunction(problem_, xk));
workingGraph.push_back(problem_.equalities);
for (const LinearInequality::shared_ptr& factor : workingSet)
if (factor->active()) workingGraph.push_back(factor);
return workingGraph;
}
//******************************************************************************
Template typename This::State This::iterate(
const typename This::State& state) const {
// Algorithm 16.3 from Nocedal06book.
// Solve with the current working set eqn 16.39, but instead of solving for p
// solve for x
GaussianFactorGraph workingGraph =
buildWorkingGraph(state.workingSet, state.values);
VectorValues newValues = workingGraph.optimize();
// If we CAN'T move further
// if p_k = 0 is the original condition, modified by Duy to say that the state
// update is zero.
if (newValues.equals(state.values, 1e-7)) {
// Compute lambda from the dual graph
GaussianFactorGraph::shared_ptr dualGraph = buildDualGraph(state.workingSet,
newValues);
VectorValues duals = dualGraph->optimize();
int leavingFactor = identifyLeavingConstraint(state.workingSet, duals);
// If all inequality constraints are satisfied: We have the solution!!
if (leavingFactor < 0) {
return State(newValues, duals, state.workingSet, true,
state.iterations + 1);
} else {
// Inactivate the leaving constraint
InequalityFactorGraph newWorkingSet = state.workingSet;
newWorkingSet.at(leavingFactor)->inactivate();
return State(newValues, duals, newWorkingSet, false,
state.iterations + 1);
}
} else {
// If we CAN make some progress, i.e. p_k != 0
// Adapt stepsize if some inactive constraints complain about this move
double alpha;
int factorIx;
VectorValues p = newValues - state.values;
boost::tie(alpha, factorIx) = // using 16.41
computeStepSize(state.workingSet, state.values, p, POLICY::maxAlpha);
// also add to the working set the one that complains the most
InequalityFactorGraph newWorkingSet = state.workingSet;
if (factorIx >= 0)
newWorkingSet.at(factorIx)->activate();
// step!
newValues = state.values + alpha * p;
return State(newValues, state.duals, newWorkingSet, false,
state.iterations + 1);
}
}
//******************************************************************************
Template InequalityFactorGraph This::identifyActiveConstraints(
const InequalityFactorGraph& inequalities,
const VectorValues& initialValues, const VectorValues& duals,
bool useWarmStart) const {
InequalityFactorGraph workingSet;
for (const LinearInequality::shared_ptr& factor : inequalities) {
LinearInequality::shared_ptr workingFactor(new LinearInequality(*factor));
if (useWarmStart && duals.size() > 0) {
if (duals.exists(workingFactor->dualKey())) workingFactor->activate();
else workingFactor->inactivate();
} else {
double error = workingFactor->error(initialValues);
// Safety guard. This should not happen unless users provide a bad init
if (error > 0) throw InfeasibleInitialValues();
if (fabs(error) < 1e-7)
workingFactor->activate();
else
workingFactor->inactivate();
}
workingSet.push_back(workingFactor);
}
return workingSet;
}
//******************************************************************************
Template std::pair<VectorValues, VectorValues> This::optimize(
const VectorValues& initialValues, const VectorValues& duals,
bool useWarmStart) const {
// Initialize workingSet from the feasible initialValues
InequalityFactorGraph workingSet = identifyActiveConstraints(
problem_.inequalities, initialValues, duals, useWarmStart);
State state(initialValues, duals, workingSet, false, 0);
/// main loop of the solver
while (!state.converged) state = iterate(state);
return std::make_pair(state.values, state.duals);
}
//******************************************************************************
Template std::pair<VectorValues, VectorValues> This::optimize() const {
INITSOLVER initSolver(problem_);
VectorValues initValues = initSolver.solve();
return optimize(initValues);
}
}
#undef Template
#undef This

132
gtsam_unstable/linear/ActiveSetSolver.cpp
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@ -1,132 +0,0 @@
/**
* @file ActiveSetSolver.cpp
* @brief Implmentation of ActiveSetSolver.
* @author Ivan Dario Jimenez
* @author Duy Nguyen Ta
* @date 2/11/16
*/
#include <gtsam_unstable/linear/ActiveSetSolver.h>
namespace gtsam {
/*
* The goal of this function is to find currently active inequality constraints
* that violate the condition to be active. The one that violates the condition
* the most will be removed from the active set. See Nocedal06book, pg 469-471
*
* Find the BAD active inequality that pulls x strongest to the wrong direction
* of its constraint (i.e. it is pulling towards >0, while its feasible region is <=0)
*
* For active inequality constraints (those that are enforced as equality constraints
* in the current working set), we want lambda < 0.
* This is because:
* - From the Lagrangian L = f - lambda*c, we know that the constraint force
* is (lambda * \grad c) = \grad f. Intuitively, to keep the solution x stay
* on the constraint surface, the constraint force has to balance out with
* other unconstrained forces that are pulling x towards the unconstrained
* minimum point. The other unconstrained forces are pulling x toward (-\grad f),
* hence the constraint force has to be exactly \grad f, so that the total
* force is 0.
* - We also know that at the constraint surface c(x)=0, \grad c points towards + (>= 0),
* while we are solving for - (<=0) constraint.
* - We want the constraint force (lambda * \grad c) to pull x towards the - (<=0) direction
* i.e., the opposite direction of \grad c where the inequality constraint <=0 is satisfied.
* That means we want lambda < 0.
* - This is because when the constrained force pulls x towards the infeasible region (+),
* the unconstrained force is pulling x towards the opposite direction into
* the feasible region (again because the total force has to be 0 to make x stay still)
* So we can drop this constraint to have a lower error but feasible solution.
*
* In short, active inequality constraints with lambda > 0 are BAD, because they
* violate the condition to be active.
*
* And we want to remove the worst one with the largest lambda from the active set.
*
*/
int ActiveSetSolver::identifyLeavingConstraint(
const InequalityFactorGraph& workingSet,
const VectorValues& lambdas) const {
int worstFactorIx = -1;
// preset the maxLambda to 0.0: if lambda is <= 0.0, the constraint is
// either
// inactive or a good inequality constraint, so we don't care!
double maxLambda = 0.0;
for (size_t factorIx = 0; factorIx < workingSet.size(); ++factorIx) {
const LinearInequality::shared_ptr& factor = workingSet.at(factorIx);
if (factor->active()) {
double lambda = lambdas.at(factor->dualKey())[0];
if (lambda > maxLambda) {
worstFactorIx = factorIx;
maxLambda = lambda;
}
}
}
return worstFactorIx;
}
/* This function will create a dual graph that solves for the
* lagrange multipliers for the current working set.
* You can use lagrange multipliers as a necessary condition for optimality.
* The factor graph that is being solved is f' = -lambda * g'
* where f is the optimized function and g is the function resulting from
* aggregating the working set.
* The lambdas give you information about the feasibility of a constraint.
* if lambda < 0 the constraint is Ok
* if lambda = 0 you are on the constraint
* if lambda > 0 you are violating the constraint.
*/
GaussianFactorGraph::shared_ptr ActiveSetSolver::buildDualGraph(
const InequalityFactorGraph& workingSet, const VectorValues& delta) const {
GaussianFactorGraph::shared_ptr dualGraph(new GaussianFactorGraph());
for (Key key : constrainedKeys_) {
// Each constrained key becomes a factor in the dual graph
JacobianFactor::shared_ptr dualFactor = createDualFactor(key, workingSet,
delta);
if (!dualFactor->empty())
dualGraph->push_back(dualFactor);
}
return dualGraph;
}
/*
* Compute step size alpha for the new solution x' = xk + alpha*p, where alpha \in [0,1]
*
* @return a tuple of (alpha, factorIndex, sigmaIndex) where (factorIndex, sigmaIndex)
* is the constraint that has minimum alpha, or (-1,-1) if alpha = 1.
* This constraint will be added to the working set and become active
* in the next iteration.
*/
boost::tuple<double, int> ActiveSetSolver::computeStepSize(
const InequalityFactorGraph& workingSet, const VectorValues& xk,
const VectorValues& p, const double& startAlpha) const {
double minAlpha = startAlpha;
int closestFactorIx = -1;
for (size_t factorIx = 0; factorIx < workingSet.size(); ++factorIx) {
const LinearInequality::shared_ptr& factor = workingSet.at(factorIx);
double b = factor->getb()[0];
// only check inactive factors
if (!factor->active()) {
// Compute a'*p
double aTp = factor->dotProductRow(p);
// Check if a'*p >0. Don't care if it's not.
if (aTp <= 0)
continue;
// Compute a'*xk
double aTx = factor->dotProductRow(xk);
// alpha = (b - a'*xk) / (a'*p)
double alpha = (b - aTx) / aTp;
// We want the minimum of all those max alphas
if (alpha < minAlpha) {
closestFactorIx = factorIx;
minAlpha = alpha;
}
}
}
return boost::make_tuple(minAlpha, closestFactorIx);
}
}

165
gtsam_unstable/linear/ActiveSetSolver.h
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@ -1,6 +1,17 @@
/* ----------------------------------------------------------------------------
* 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 ActiveSetSolver.h * @file ActiveSetSolver.h
* @brief Abstract class above for solving problems with the abstract set method. * @brief Active set method for solving LP, QP problems
* @author Ivan Dario Jimenez * @author Ivan Dario Jimenez
* @author Duy Nguyen Ta * @author Duy Nguyen Ta
* @date 1/25/16 * @date 1/25/16
@ -14,26 +25,101 @@
namespace gtsam { namespace gtsam {
/** /**
* This is a base class for all implementations of the active set algorithm for solving * This class implementations the active set algorithm for solving convex
* Programming problems. It provides services and variables all active set implementations * Programming problems.
* share. *
* @tparam PROBLEM Type of the problem to solve, e.g. LP (linear program) or
* QP (quadratic program).
* @tparam POLICY specific detail policy tailored for the particular program
* @tparam INITSOLVER Solver for an initial feasible solution of this problem.
*/ */
template <class PROBLEM, class POLICY, class INITSOLVER>
class ActiveSetSolver { class ActiveSetSolver {
public:
/**
* This struct contains the state information for a single iteration of an
* active set method iteration.
*/
struct State {
VectorValues values; //!< current best values at each step
VectorValues duals; //!< current values of dual variables at each step
InequalityFactorGraph workingSet; /*!< keep track of current active/inactive
inequality constraints */
bool converged; //!< True if the algorithm has converged to a solution
size_t iterations; //!< number of iterations. Incremented at the end of
//each iter.
/// Default constructor
State()
: values(), duals(), workingSet(), converged(false), iterations(0) {}
/// Constructor with initial values
State(const VectorValues& initialValues, const VectorValues& initialDuals,
const InequalityFactorGraph& initialWorkingSet, bool _converged,
size_t _iterations)
: values(initialValues),
duals(initialDuals),
workingSet(initialWorkingSet),
converged(_converged),
iterations(_iterations) {}
};
protected: protected:
KeySet constrainedKeys_; //!< all constrained keys, will become factors in dual graphs const PROBLEM& problem_; //!< the particular [convex] problem to solve
VariableIndex equalityVariableIndex_, VariableIndex equalityVariableIndex_,
inequalityVariableIndex_; //!< index to corresponding factors to build dual graphs inequalityVariableIndex_; /*!< index to corresponding factors to build
dual graphs */
KeySet constrainedKeys_; /*!< all constrained keys, will become factors in
dual graphs */
/// Vector of key matrix pairs. Matrices are usually the A term for a factor.
typedef std::vector<std::pair<Key, Matrix> > TermsContainer;
public: public:
typedef std::vector<std::pair<Key, Matrix> > TermsContainer; //!< vector of key matrix pairs /// Constructor
//Matrices are usually the A term for a factor. ActiveSetSolver(const PROBLEM& problem) : problem_(problem) {
equalityVariableIndex_ = VariableIndex(problem_.equalities);
inequalityVariableIndex_ = VariableIndex(problem_.inequalities);
constrainedKeys_ = problem_.equalities.keys();
constrainedKeys_.merge(problem_.inequalities.keys());
}
/** /**
* Creates a dual factor from the current workingSet and the key of the * Optimize with provided initial values
* the variable used to created the dual factor. * For this version, it is the responsibility of the caller to provide
* a feasible initial value, otherwise, an exception will be thrown.
* @return a pair of <primal, dual> solutions
*/ */
virtual JacobianFactor::shared_ptr createDualFactor(Key key, std::pair<VectorValues, VectorValues> optimize(
const InequalityFactorGraph& workingSet, const VectorValues& initialValues,
const VectorValues& delta) const = 0; const VectorValues& duals = VectorValues(),
bool useWarmStart = false) const;
/**
* For this version the caller will not have to provide an initial value
* @return a pair of <primal, dual> solutions
*/
std::pair<VectorValues, VectorValues> optimize() const;
protected:
/**
* Compute minimum step size alpha to move from the current point @p xk to the
* next feasible point along a direction @p p: x' = xk + alpha*p,
* where alpha \in [0,maxAlpha].
*
* For QP, maxAlpha = 1. For LP: maxAlpha = Inf.
*
* @return a tuple of (minAlpha, closestFactorIndex) where closestFactorIndex
* is the closest inactive inequality constraint that blocks xk to move
* further and that has the minimum alpha, or (-1, maxAlpha) if there is no
* such inactive blocking constraint.
*
* If there is a blocking constraint, the closest one will be added to the
* working set and become active in the next iteration.
*/
boost::tuple<double, int> computeStepSize(
const InequalityFactorGraph& workingSet, const VectorValues& xk,
const VectorValues& p, const double& maxAlpha) const;
/** /**
* Finds the active constraints in the given factor graph and returns the * Finds the active constraints in the given factor graph and returns the
@ -59,35 +145,46 @@ public:
} }
return Aterms; return Aterms;
} }
/**
* Identifies active constraints that shouldn't be active anymore.
*/
int identifyLeavingConstraint(const InequalityFactorGraph& workingSet,
const VectorValues& lambdas) const;
/** /**
* Builds a dual graph from the current working set. * Creates a dual factor from the current workingSet and the key of the
* the variable used to created the dual factor.
*/ */
JacobianFactor::shared_ptr createDualFactor(
Key key, const InequalityFactorGraph& workingSet,
const VectorValues& delta) const;
public: /// Just for testing...
/// Builds a dual graph from the current working set.
GaussianFactorGraph::shared_ptr buildDualGraph( GaussianFactorGraph::shared_ptr buildDualGraph(
const InequalityFactorGraph& workingSet, const VectorValues& delta) const; const InequalityFactorGraph& workingSet, const VectorValues& delta) const;
protected:
/** /**
* Protected constructor because this class doesn't have any meaning without * Build a working graph of cost, equality and active inequality constraints
* a concrete Programming problem to solve. * to solve at each iteration.
* @param workingSet the collection of all cost and constrained factors
* @param xk current solution, used to build a special quadratic cost in LP
* @return a new better solution
*/ */
ActiveSetSolver() : GaussianFactorGraph buildWorkingGraph(
constrainedKeys_() { const InequalityFactorGraph& workingSet,
} const VectorValues& xk = VectorValues()) const;
/// Iterate 1 step, return a new state with a new workingSet and values
State iterate(const State& state) const;
/// Identify active constraints based on initial values.
InequalityFactorGraph identifyActiveConstraints(
const InequalityFactorGraph& inequalities,
const VectorValues& initialValues,
const VectorValues& duals = VectorValues(),
bool useWarmStart = false) const;
/// Identifies active constraints that shouldn't be active anymore.
int identifyLeavingConstraint(const InequalityFactorGraph& workingSet,
const VectorValues& lambdas) const;
/**
* Computes the distance to move from the current point being examined to the next
* location to be examined by the graph. This should only be used where there are less
* than two constraints active.
*/
boost::tuple<double, int> computeStepSize(
const InequalityFactorGraph& workingSet, const VectorValues& xk,
const VectorValues& p, const double& startAlpha) const;
}; };
/** /**
@ -106,3 +203,5 @@ Key maxKey(const PROBLEM& problem) {
} }
} // namespace gtsam } // namespace gtsam
#include <gtsam_unstable/linear/ActiveSetSolver-inl.h>

11
gtsam_unstable/linear/InfeasibleInitialValues.h
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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 InfeasibleInitialValues.h * @file InfeasibleInitialValues.h
* @brief Exception thrown when given Infeasible Initial Values. * @brief Exception thrown when given Infeasible Initial Values.

13
gtsam_unstable/linear/InfeasibleOrUnboundedProblem.h
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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 InfeasibleOrUnboundedProblem.h * @file InfeasibleOrUnboundedProblem.h
* @brief Throw when the problem is either infeasible or unbounded * @brief Throw when the problem is either infeasible or unbounded
@ -5,6 +16,8 @@
* @date 1/24/16 * @date 1/24/16
*/ */
#pragma once
namespace gtsam { namespace gtsam {
class InfeasibleOrUnboundedProblem: public ThreadsafeException< class InfeasibleOrUnboundedProblem: public ThreadsafeException<

1
gtsam_unstable/linear/LPInitSolver.cpp
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@ -19,6 +19,7 @@
#include <gtsam_unstable/linear/LPInitSolver.h> #include <gtsam_unstable/linear/LPInitSolver.h>
#include <gtsam_unstable/linear/LPSolver.h> #include <gtsam_unstable/linear/LPSolver.h>
#include <gtsam_unstable/linear/InfeasibleOrUnboundedProblem.h>
namespace gtsam { namespace gtsam {

2
gtsam_unstable/linear/LPInitSolver.h
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@ -21,7 +21,7 @@
#include <gtsam_unstable/linear/LP.h> #include <gtsam_unstable/linear/LP.h>
#include <gtsam_unstable/linear/InfeasibleOrUnboundedProblem.h> #include <gtsam_unstable/linear/InfeasibleOrUnboundedProblem.h>
#include <gtsam_unstable/linear/QPSolver.h> #include <gtsam/linear/GaussianFactorGraph.h>
#include <CppUnitLite/Test.h> #include <CppUnitLite/Test.h>
namespace gtsam { namespace gtsam {

193
gtsam_unstable/linear/LPSolver.cpp
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@ -18,199 +18,8 @@
*/ */
#include <gtsam_unstable/linear/LPSolver.h> #include <gtsam_unstable/linear/LPSolver.h>
#include <gtsam_unstable/linear/LPInitSolver.h>
#include <gtsam_unstable/linear/InfeasibleInitialValues.h>
namespace gtsam { namespace gtsam {
//****************************************************************************** constexpr double LPPolicy::maxAlpha;
LPSolver::LPSolver(const LP &lp) :
lp_(lp) {
equalityVariableIndex_ = VariableIndex(lp_.equalities);
inequalityVariableIndex_ = VariableIndex(lp_.inequalities);
constrainedKeys_ = lp_.equalities.keys();
constrainedKeys_.merge(lp_.inequalities.keys());
}
//******************************************************************************
GaussianFactorGraph LPSolver::buildCostFunction(const VectorValues &xk) const {
GaussianFactorGraph graph;
for (LinearCost::const_iterator it = lp_.cost.begin(); it != lp_.cost.end();
++it) {
size_t dim = lp_.cost.getDim(it);
Vector b = xk.at(*it) - lp_.cost.getA(it).transpose(); // b = xk-g
graph.push_back(JacobianFactor(*it, Matrix::Identity(dim, dim), b));
}
KeySet allKeys = lp_.inequalities.keys();
allKeys.merge(lp_.equalities.keys());
allKeys.merge(KeySet(lp_.cost.keys()));
// Add corresponding factors for all variables that are not explicitly in the
// cost function. Gradients of the cost function wrt to these variables are
// zero (g=0), so b=xk
if (lp_.cost.keys().size() != allKeys.size()) {
KeySet difference;
std::set_difference(allKeys.begin(), allKeys.end(), lp_.cost.begin(),
lp_.cost.end(), std::inserter(difference, difference.end()));
for (Key k : difference) {
size_t dim = lp_.constrainedKeyDimMap().at(k);
graph.push_back(JacobianFactor(k, Matrix::Identity(dim, dim), xk.at(k)));
}
}
return graph;
}
//******************************************************************************
GaussianFactorGraph LPSolver::buildWorkingGraph(
const InequalityFactorGraph &workingSet, const VectorValues &xk) const {
GaussianFactorGraph workingGraph;
// || X - Xk + g ||^2
workingGraph.push_back(buildCostFunction(xk));
workingGraph.push_back(lp_.equalities);
for (const LinearInequality::shared_ptr &factor : workingSet) {
if (factor->active()) workingGraph.push_back(factor);
}
return workingGraph;
}
//******************************************************************************
LPState LPSolver::iterate(const LPState &state) const {
// Solve with the current working set
// LP: project the objective neg. gradient to the constraint's null space
// to find the direction to move
GaussianFactorGraph workingGraph =
buildWorkingGraph(state.workingSet, state.values);
VectorValues newValues = workingGraph.optimize();
// If we CAN'T move further
// LP: projection on the constraints' nullspace is zero: we are at a vertex
if (newValues.equals(state.values, 1e-7)) {
// Find and remove the bad inequality constraint by computing its lambda
// Compute lambda from the dual graph
// LP: project the objective's gradient onto each constraint gradient to
// obtain the dual scaling factors
// is it true??
GaussianFactorGraph::shared_ptr dualGraph = buildDualGraph(state.workingSet,
newValues);
VectorValues duals = dualGraph->optimize();
// LP: see which inequality constraint has wrong pulling direction, i.e., dual < 0
int leavingFactor = identifyLeavingConstraint(state.workingSet, duals);
// If all inequality constraints are satisfied: We have the solution!!
if (leavingFactor < 0) {
// TODO If we still have infeasible equality constraints: the problem is
// over-constrained. No solution!
// ...
return LPState(newValues, duals, state.workingSet, true,
state.iterations + 1);
} else {
// Inactivate the leaving constraint
// LP: remove the bad ineq constraint out of the working set
InequalityFactorGraph newWorkingSet = state.workingSet;
newWorkingSet.at(leavingFactor)->inactivate();
return LPState(newValues, duals, newWorkingSet, false,
state.iterations + 1);
}
} else {
// If we CAN make some progress, i.e. p_k != 0
// Adapt stepsize if some inactive constraints complain about this move
// LP: projection on nullspace is NOT zero:
// find and put a blocking inactive constraint to the working set,
// otherwise the problem is unbounded!!!
double alpha;
int factorIx;
VectorValues p = newValues - state.values;
// GTSAM_PRINT(p);
boost::tie(alpha, factorIx) = // using 16.41
computeStepSize(state.workingSet, state.values, p);
// also add to the working set the one that complains the most
InequalityFactorGraph newWorkingSet = state.workingSet;
if (factorIx >= 0)
newWorkingSet.at(factorIx)->activate();
// step!
newValues = state.values + alpha * p;
return LPState(newValues, state.duals, newWorkingSet, false,
state.iterations + 1);
}
}
//******************************************************************************
boost::shared_ptr<JacobianFactor> LPSolver::createDualFactor(
Key key, const InequalityFactorGraph &workingSet,
const VectorValues &delta) const {
// Transpose the A matrix of constrained factors to have the jacobian of the
// dual key
TermsContainer Aterms = collectDualJacobians<LinearEquality>(
key, lp_.equalities, equalityVariableIndex_);
TermsContainer AtermsInequalities = collectDualJacobians<LinearInequality>(
key, workingSet, inequalityVariableIndex_);
Aterms.insert(Aterms.end(), AtermsInequalities.begin(),
AtermsInequalities.end());
// Collect the gradients of unconstrained cost factors to the b vector
if (Aterms.size() > 0) {
Vector b = lp_.costGradient(key, delta);
// to compute the least-square approximation of dual variables
return boost::make_shared<JacobianFactor>(Aterms, b);
} else {
return boost::make_shared<JacobianFactor>();
}
}
//******************************************************************************
InequalityFactorGraph LPSolver::identifyActiveConstraints(
const InequalityFactorGraph &inequalities,
const VectorValues &initialValues, const VectorValues &duals,
bool useWarmStart) const {
InequalityFactorGraph workingSet;
for (const LinearInequality::shared_ptr &factor : inequalities) {
LinearInequality::shared_ptr workingFactor(new LinearInequality(*factor));
if (useWarmStart && duals.size() > 0) {
if (duals.exists(workingFactor->dualKey())) workingFactor->activate();
else workingFactor->inactivate();
} else {
double error = workingFactor->error(initialValues);
// Safety guard. This should not happen unless users provide a bad init
if (error > 0) throw InfeasibleInitialValues();
if (fabs(error) < 1e-7)
workingFactor->activate();
else
workingFactor->inactivate();
}
workingSet.push_back(workingFactor);
}
return workingSet;
}
//******************************************************************************
std::pair<VectorValues, VectorValues> LPSolver::optimize(
const VectorValues &initialValues, const VectorValues &duals,
bool useWarmStart) const {
{
// Initialize workingSet from the feasible initialValues
InequalityFactorGraph workingSet = identifyActiveConstraints(
lp_.inequalities, initialValues, duals, useWarmStart);
LPState state(initialValues, duals, workingSet, false, 0);
/// main loop of the solver
while (!state.converged) {
state = iterate(state);
}
return make_pair(state.values, state.duals);
}
}
//******************************************************************************
boost::tuples::tuple<double, int> LPSolver::computeStepSize(
const InequalityFactorGraph &workingSet, const VectorValues &xk,
const VectorValues &p) const {
return ActiveSetSolver::computeStepSize(workingSet, xk, p,
std::numeric_limits<double>::infinity());
}
//******************************************************************************
pair<VectorValues, VectorValues> LPSolver::optimize() const {
LPInitSolver initSolver(lp_);
VectorValues initValues = initSolver.solve();
return optimize(initValues);
}
} }

106
gtsam_unstable/linear/LPSolver.h
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@ -11,39 +11,25 @@
/** /**
* @file LPSolver.h * @file LPSolver.h
* @brief Class used to solve Linear Programming Problems as defined in LP.h * @brief Policy of ActiveSetSolver to solve Linear Programming Problems
* @author Duy Nguyen Ta * @author Duy Nguyen Ta
* @author Ivan Dario Jimenez * @author Ivan Dario Jimenez
* @date 1/24/16 * @date 6/16/16
*/ */
#pragma once
#include <gtsam_unstable/linear/LPState.h>
#include <gtsam_unstable/linear/LP.h> #include <gtsam_unstable/linear/LP.h>
#include <gtsam_unstable/linear/ActiveSetSolver.h> #include <gtsam_unstable/linear/ActiveSetSolver.h>
#include <gtsam_unstable/linear/LinearCost.h> #include <gtsam_unstable/linear/LPInitSolver.h>
#include <gtsam/linear/VectorValues.h>
#include <limits>
#include <algorithm>
namespace gtsam { namespace gtsam {
class LPSolver: public ActiveSetSolver { /// Policy for ActivetSetSolver to solve Linear Programming \sa LP problems
const LP &lp_; //!< the linear programming problem struct LPPolicy {
public: /// Maximum alpha for line search. For LP, it's infinity
/// Constructor static constexpr double maxAlpha = std::numeric_limits<double>::infinity();
LPSolver(const LP &lp);
const LP &lp() const {
return lp_;
}
/*
* This function performs an iteration of the Active Set Method for solving
* LP problems. At the end of this iteration the problem should either be found
* to be unfeasible, solved or the current state changed to reflect a new
* working set.
*/
LPState iterate(const LPState &state) const;
/** /**
* Create the factor ||x-xk - (-g)||^2 where xk is the current feasible solution * Create the factor ||x-xk - (-g)||^2 where xk is the current feasible solution
@ -57,45 +43,37 @@ public:
* The least-square solution of this quadratic subject to a set of linear constraints * The least-square solution of this quadratic subject to a set of linear constraints
* is the projection of the gradient onto the constraints' subspace * is the projection of the gradient onto the constraints' subspace
*/ */
GaussianFactorGraph buildCostFunction(const VectorValues &xk) const; static GaussianFactorGraph buildCostFunction(const LP& lp,
const VectorValues& xk) {
GaussianFactorGraph graph;
for (LinearCost::const_iterator it = lp.cost.begin(); it != lp.cost.end();
++it) {
size_t dim = lp.cost.getDim(it);
Vector b = xk.at(*it) - lp.cost.getA(it).transpose(); // b = xk-g
graph.push_back(JacobianFactor(*it, Matrix::Identity(dim, dim), b));
}
GaussianFactorGraph buildWorkingGraph( KeySet allKeys = lp.inequalities.keys();
const InequalityFactorGraph& workingSet, const VectorValues& xk) const; allKeys.merge(lp.equalities.keys());
allKeys.merge(KeySet(lp.cost.keys()));
/* // Add corresponding factors for all variables that are not explicitly in
* A dual factor takes the objective function and a set of constraints. // the cost function. Gradients of the cost function wrt to these variables
* It then creates a least-square approximation of the lagrangian multipliers // are zero (g=0), so b=xk
* for the following problem: f' = - lambda * g' where f is the objection if (lp.cost.keys().size() != allKeys.size()) {
* function g are dual factors and lambda is the lagrangian multiplier. KeySet difference;
*/ std::set_difference(allKeys.begin(), allKeys.end(), lp.cost.begin(),
JacobianFactor::shared_ptr createDualFactor(Key key, lp.cost.end(),
const InequalityFactorGraph &workingSet, const VectorValues &delta) const; std::inserter(difference, difference.end()));
for (Key k : difference) {
/// TODO(comment) size_t dim = lp.constrainedKeyDimMap().at(k);
boost::tuple<double, int> computeStepSize( graph.push_back(
const InequalityFactorGraph &workingSet, const VectorValues &xk, JacobianFactor(k, Matrix::Identity(dim, dim), xk.at(k)));
const VectorValues &p) const; }
}
/* return graph;
* Given an initial value this function determine which constraints are active }
* which can be used to initialize the working set.
* A constraint Ax <= b is active if we have an x' s.t. Ax' = b
*/
InequalityFactorGraph identifyActiveConstraints(
const InequalityFactorGraph &inequalities,
const VectorValues &initialValues, const VectorValues &duals,
bool useWarmStart = false) const;
/** Optimize with the provided feasible initial values
* TODO: throw exception if the initial values is not feasible wrt inequality constraints
* TODO: comment duals
*/
pair<VectorValues, VectorValues> optimize(const VectorValues &initialValues,
const VectorValues &duals = VectorValues(), bool useWarmStart = false) const;
/**
* Optimize without initial values.
*/
pair<VectorValues, VectorValues> optimize() const;
}; };
} // namespace gtsam
using LPSolver = ActiveSetSolver<LP, LPPolicy, LPInitSolver>;
}

44
gtsam_unstable/linear/LPState.h
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@ -1,44 +0,0 @@
/**
* @file LPState.h
* @brief This struct holds the state of QPSolver at each iteration
* @author Ivan Dario Jimenez
* @date 1/24/16
*/
#include <gtsam/linear/VectorValues.h>
#include "InequalityFactorGraph.h"
namespace gtsam {
/*
* This struct contains the state information for a single iteration of an
* active set method iteration.
*/
struct LPState {
// A itermediate value for the value of the final solution.
VectorValues values;
// Constains the set of duals computed during the iteration that retuned this
// state.
VectorValues duals;
// An inequality Factor Graph that contains only the active constriants.
InequalityFactorGraph workingSet;
// True if the algorithm has converged to a solution
bool converged;
// counter for the number of iteration. Incremented at the end of each iter.
size_t iterations;
/// default constructor
LPState() :
values(), duals(), workingSet(), converged(false), iterations(0) {
}
/// constructor with initial values
LPState(const VectorValues& initialValues, const VectorValues& initialDuals,
const InequalityFactorGraph& initialWorkingSet, bool _converged,
size_t _iterations) :
values(initialValues), duals(initialDuals), workingSet(initialWorkingSet), converged(
_converged), iterations(_iterations) {
}
};
}

152
gtsam_unstable/linear/QPSolver.cpp
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@ -17,157 +17,7 @@
*/ */
#include <gtsam_unstable/linear/QPSolver.h> #include <gtsam_unstable/linear/QPSolver.h>
#include <gtsam_unstable/linear/QPInitSolver.h>
#include <gtsam_unstable/linear/InfeasibleInitialValues.h>
using namespace std;
namespace gtsam { namespace gtsam {
constexpr double QPPolicy::maxAlpha;
//******************************************************************************
QPSolver::QPSolver(const QP& qp) :
qp_(qp) {
equalityVariableIndex_ = VariableIndex(qp_.equalities);
inequalityVariableIndex_ = VariableIndex(qp_.inequalities);
constrainedKeys_ = qp_.equalities.keys();
constrainedKeys_.merge(qp_.inequalities.keys());
} }
//******************************************************************************
GaussianFactorGraph QPSolver::buildWorkingGraph(
const InequalityFactorGraph& workingSet, const VectorValues& xk) const {
GaussianFactorGraph workingGraph;
workingGraph.push_back(buildCostFunction(xk));
workingGraph.push_back(qp_.equalities);
for (const LinearInequality::shared_ptr& factor : workingSet) {
if (factor->active()) workingGraph.push_back(factor);
}
return workingGraph;
}
//******************************************************************************
JacobianFactor::shared_ptr QPSolver::createDualFactor(
Key key, const InequalityFactorGraph& workingSet,
const VectorValues& delta) const {
// Transpose the A matrix of constrained factors to have the jacobian of the
// dual key
TermsContainer Aterms = collectDualJacobians<LinearEquality>(
key, qp_.equalities, equalityVariableIndex_);
TermsContainer AtermsInequalities = collectDualJacobians<LinearInequality>(
key, workingSet, inequalityVariableIndex_);
Aterms.insert(Aterms.end(), AtermsInequalities.begin(),
AtermsInequalities.end());
// Collect the gradients of unconstrained cost factors to the b vector
if (Aterms.size() > 0) {
Vector b = qp_.costGradient(key, delta);
// to compute the least-square approximation of dual variables
return boost::make_shared<JacobianFactor>(Aterms, b);
} else {
return boost::make_shared<JacobianFactor>();
}
}
//******************************************************************************
boost::tuple<double, int> QPSolver::computeStepSize(
const InequalityFactorGraph& workingSet, const VectorValues& xk,
const VectorValues& p) const {
return ActiveSetSolver::computeStepSize(workingSet, xk, p, 1);
}
//******************************************************************************
QPState QPSolver::iterate(const QPState& state) const {
// Algorithm 16.3 from Nocedal06book.
// Solve with the current working set eqn 16.39, but instead of solving for p
// solve for x
GaussianFactorGraph workingGraph =
buildWorkingGraph(state.workingSet, state.values);
VectorValues newValues = workingGraph.optimize();
// If we CAN'T move further
// if p_k = 0 is the original condition, modified by Duy to say that the state
// update is zero.
if (newValues.equals(state.values, 1e-7)) {
// Compute lambda from the dual graph
GaussianFactorGraph::shared_ptr dualGraph = buildDualGraph(state.workingSet,
newValues);
VectorValues duals = dualGraph->optimize();
int leavingFactor = identifyLeavingConstraint(state.workingSet, duals);
// If all inequality constraints are satisfied: We have the solution!!
if (leavingFactor < 0) {
return QPState(newValues, duals, state.workingSet, true,
state.iterations + 1);
} else {
// Inactivate the leaving constraint
InequalityFactorGraph newWorkingSet = state.workingSet;
newWorkingSet.at(leavingFactor)->inactivate();
return QPState(newValues, duals, newWorkingSet, false,
state.iterations + 1);
}
} else {
// If we CAN make some progress, i.e. p_k != 0
// Adapt stepsize if some inactive constraints complain about this move
double alpha;
int factorIx;
VectorValues p = newValues - state.values;
boost::tie(alpha, factorIx) = // using 16.41
computeStepSize(state.workingSet, state.values, p);
// also add to the working set the one that complains the most
InequalityFactorGraph newWorkingSet = state.workingSet;
if (factorIx >= 0)
newWorkingSet.at(factorIx)->activate();
// step!
newValues = state.values + alpha * p;
return QPState(newValues, state.duals, newWorkingSet, false,
state.iterations + 1);
}
}
//******************************************************************************
InequalityFactorGraph QPSolver::identifyActiveConstraints(
const InequalityFactorGraph& inequalities,
const VectorValues& initialValues, const VectorValues& duals,
bool useWarmStart) const {
InequalityFactorGraph workingSet;
for (const LinearInequality::shared_ptr& factor : inequalities) {
LinearInequality::shared_ptr workingFactor(new LinearInequality(*factor));
if (useWarmStart && duals.size() > 0) {
if (duals.exists(workingFactor->dualKey())) workingFactor->activate();
else workingFactor->inactivate();
} else {
double error = workingFactor->error(initialValues);
// Safety guard. This should not happen unless users provide a bad init
if (error > 0) throw InfeasibleInitialValues();
if (fabs(error) < 1e-7)
workingFactor->activate();
else
workingFactor->inactivate();
}
workingSet.push_back(workingFactor);
}
return workingSet;
}
//******************************************************************************
pair<VectorValues, VectorValues> QPSolver::optimize(
const VectorValues& initialValues, const VectorValues& duals,
bool useWarmStart) const {
// Initialize workingSet from the feasible initialValues
InequalityFactorGraph workingSet = identifyActiveConstraints(qp_.inequalities,
initialValues, duals, useWarmStart);
QPState state(initialValues, duals, workingSet, false, 0);
/// main loop of the solver
while (!state.converged)
state = iterate(state);
return make_pair(state.values, state.duals);
}
//******************************************************************************
pair<VectorValues, VectorValues> QPSolver::optimize() const {
QPInitSolver initSolver(qp_);
VectorValues initValues = initSolver.solve();
return optimize(initValues);
}
} /* namespace gtsam */

97
gtsam_unstable/linear/QPSolver.h
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@ -11,95 +11,32 @@
/** /**
* @file QPSolver.h * @file QPSolver.h
* @brief A quadratic programming solver implements the active set method * @brief Policy of ActiveSetSolver to solve Quadratic Programming Problems
* @date Apr 15, 2014 * @author Duy Nguyen Ta
* @author Ivan Dario Jimenez * @author Ivan Dario Jimenez
* @author Duy-Nguyen Ta * @date 6/16/16
*/ */
#pragma once
#include <gtsam_unstable/linear/QP.h> #include <gtsam_unstable/linear/QP.h>
#include <gtsam_unstable/linear/ActiveSetSolver.h> #include <gtsam_unstable/linear/ActiveSetSolver.h>
#include <gtsam_unstable/linear/QPState.h> #include <gtsam_unstable/linear/QPInitSolver.h>
#include <gtsam/linear/VectorValues.h> #include <limits>
#include <algorithm>
#include <vector>
#include <set>
namespace gtsam { namespace gtsam {
/** /// Policy for ActivetSetSolver to solve Linear Programming \sa QP problems
* This QPSolver uses the active set method to solve a quadratic programming problem struct QPPolicy {
* defined in the QP struct. /// Maximum alpha for line search. For QP, it's 1
* Note: This version of QPSolver only works with a feasible initial value. static constexpr double maxAlpha = 1.0;
*/
//TODO: Remove Vector Values
class QPSolver: public ActiveSetSolver {
const QP& qp_; //!< factor graphs of the QP problem, can't be modified! /// Simply the cost of the QP problem
static const GaussianFactorGraph& buildCostFunction(
public: const QP& qp, const VectorValues& xk = VectorValues()) {
/// Constructor return qp.cost;
QPSolver(const QP& qp);
const GaussianFactorGraph& buildCostFunction(
const VectorValues& xk = VectorValues()) const {
return qp_.cost;
} }
GaussianFactorGraph buildWorkingGraph(
const InequalityFactorGraph& workingSet,
const VectorValues& xk = VectorValues()) const;
/// Create a dual factor
JacobianFactor::shared_ptr createDualFactor(Key key,
const InequalityFactorGraph& workingSet, const VectorValues& delta) const;
/* We have to make sure the new solution with alpha satisfies all INACTIVE inequality constraints
* If some inactive inequality constraints complain about the full step (alpha = 1),
* we have to adjust alpha to stay within the inequality constraints' feasible regions.
*
* For each inactive inequality j:
* - We already have: aj'*xk - bj <= 0, since xk satisfies all inequality constraints
* - We want: aj'*(xk + alpha*p) - bj <= 0
* - If aj'*p <= 0, we have: aj'*(xk + alpha*p) <= aj'*xk <= bj, for all alpha>0
* it's good!
* - We only care when aj'*p > 0. In this case, we need to choose alpha so that
* aj'*xk + alpha*aj'*p - bj <= 0 --> alpha <= (bj - aj'*xk) / (aj'*p)
* We want to step as far as possible, so we should choose alpha = (bj - aj'*xk) / (aj'*p)
*
* We want the minimum of all those alphas among all inactive inequality.
*/
boost::tuple<double, int> computeStepSize(
const InequalityFactorGraph& workingSet, const VectorValues& xk,
const VectorValues& p) const;
/// Iterate 1 step, return a new state with a new workingSet and values
QPState iterate(const QPState& state) const;
/// Identify active constraints based on initial values.
InequalityFactorGraph identifyActiveConstraints(
const InequalityFactorGraph& inequalities,
const VectorValues& initialValues, const VectorValues& duals =
VectorValues(), bool useWarmStart = true) const;
/**
* Optimize with provided initial values
* For this version, it is the responsibility of the caller to provide
* a feasible initial value, otherwise, an exception will be thrown.
* @return a pair of <primal, dual> solutions
*/
std::pair<VectorValues, VectorValues> optimize(
const VectorValues& initialValues, const VectorValues& duals =
VectorValues(), bool useWarmStart = true) const;
/**
* For this version the caller will not have to provide an initial value
* @return a pair of <primal, dual> solutions
*/
std::pair<VectorValues, VectorValues> optimize() const;
}; };
} // namespace gtsam using QPSolver = ActiveSetSolver<QP, QPPolicy, QPInitSolver>;
}

29
gtsam_unstable/linear/QPState.h
View File

@ -1,29 +0,0 @@
//
// Created by ivan on 1/25/16.
//
#pragma once
namespace gtsam {
/// This struct holds the state of QPSolver at each iteration
struct QPState {
VectorValues values;
VectorValues duals;
InequalityFactorGraph workingSet;
bool converged;
size_t iterations;
/// default constructor
QPState() :
values(), duals(), workingSet(), converged(false), iterations(0) {
}
/// constructor with initial values
QPState(const VectorValues& initialValues, const VectorValues& initialDuals,
const InequalityFactorGraph& initialWorkingSet, bool _converged,
size_t _iterations) :
values(initialValues), duals(initialDuals), workingSet(initialWorkingSet), converged(
_converged), iterations(_iterations) {
}
};
}

5
gtsam_unstable/linear/tests/testQPSolver.cpp
View File

@ -21,8 +21,6 @@
#include <gtsam_unstable/linear/QPSolver.h> #include <gtsam_unstable/linear/QPSolver.h>
#include <gtsam_unstable/linear/QPSParser.h> #include <gtsam_unstable/linear/QPSParser.h>
#include <CppUnitLite/TestHarness.h> #include <CppUnitLite/TestHarness.h>
#include <gtsam_unstable/linear/InfeasibleInitialValues.h>
#include <gtsam_unstable/linear/InfeasibleOrUnboundedProblem.h>
using namespace std; using namespace std;
using namespace gtsam; using namespace gtsam;
@ -179,7 +177,8 @@ TEST(QPSolver, iterate) {
InequalityFactorGraph workingSet = solver.identifyActiveConstraints( InequalityFactorGraph workingSet = solver.identifyActiveConstraints(
qp.inequalities, currentSolution); qp.inequalities, currentSolution);
QPState state(currentSolution, VectorValues(), workingSet, false, 100); QPSolver::State state(currentSolution, VectorValues(), workingSet, false,
100);
int it = 0; int it = 0;
while (!state.converged) { while (!state.converged) {