static double nloptObjectiveFunction(unsigned n, const double *x, double *grad, void *f_data)
{
GradientOptimizerContext *goc = (GradientOptimizerContext *) f_data;
nlopt_opt opt = (nlopt_opt) goc->extraData;
FitContext *fc = goc->fc;
assert(n == fc->numParam);
int mode = 0;
double fit = goc->solFun((double*) x, &mode);
if (grad) {
fc->iterations += 1;
if (goc->maxMajorIterations != -1 && fc->iterations >= goc->maxMajorIterations) {
nlopt_force_stop(opt);
}
}
if (grad && goc->verbose >= 2) {
mxLog("major iteration fit=%.12f", fit);
}
if (mode == -1) {
if (!goc->feasible) {
nlopt_force_stop(opt);
}
return nan("infeasible");
}
if (!grad) return fit;
Eigen::Map< Eigen::VectorXd > Epoint((double*) x, n);
Eigen::Map< Eigen::VectorXd > Egrad(grad, n);
if (fc->wanted & FF_COMPUTE_GRADIENT) {
Egrad = fc->grad;
} else if (fc->CI && fc->CI->varIndex >= 0) {
Egrad.setZero();
Egrad[fc->CI->varIndex] = fc->lowerBound? 1 : -1;
fc->grad = Egrad;
} else {
if (goc->verbose >= 3) mxLog("fd_gradient start");
fit_functional ff(*goc);
gradient_with_ref(goc->gradientAlgo, goc->gradientIterations, goc->gradientStepSize,
ff, fit, Epoint, Egrad);
fc->grad = Egrad;
}
if (goc->verbose >= 3) {
mxPrintMat("gradient", Egrad);
}
return fit;
}
void omxSD(GradientOptimizerContext &rf)
{
int maxIter = rf.maxMajorIterations;
if (maxIter == -1) maxIter = 50000;
Eigen::VectorXd currEst(rf.numFree);
rf.copyToOptimizer(currEst.data());
int iter = 0;
double priorSpeed = 1.0, shrinkage = 0.7;
rf.setupSimpleBounds();
rf.informOut = INFORM_UNINITIALIZED;
{
int mode = 0;
rf.solFun(currEst.data(), &mode);
if (mode == -1) {
rf.informOut = INFORM_STARTING_VALUES_INFEASIBLE;
return;
}
}
double refFit = rf.getFit();
rf.grad.resize(rf.numFree);
fit_functional ff(rf);
Eigen::VectorXd majorEst = currEst;
while(++iter < maxIter && !isErrorRaised()) {
gradient_with_ref(rf.gradientAlgo, 1, rf.gradientIterations, rf.gradientStepSize,
ff, refFit, majorEst, rf.grad);
if (rf.verbose >= 3) mxPrintMat("grad", rf.grad);
if(rf.grad.norm() == 0)
{
rf.informOut = INFORM_CONVERGED_OPTIMUM;
if(rf.verbose >= 2) mxLog("After %i iterations, gradient achieves zero!", iter);
break;
}
int retries = 300;
double speed = std::min(priorSpeed, 1.0);
double bestSpeed = speed;
bool foundBetter = false;
Eigen::VectorXd bestEst(majorEst.size());
Eigen::VectorXd prevEst(majorEst.size());
Eigen::VectorXd searchDir = rf.grad;
searchDir /= searchDir.norm();
prevEst.setConstant(nan("uninit"));
while (--retries > 0 && !isErrorRaised()){
Eigen::VectorXd nextEst = majorEst - speed * searchDir;
nextEst = nextEst.cwiseMax(rf.solLB).cwiseMin(rf.solUB);
if (nextEst == prevEst) break;
prevEst = nextEst;
rf.checkActiveBoxConstraints(nextEst);
int mode = 0;
double fit = rf.solFun(nextEst.data(), &mode);
if (fit < refFit) {
foundBetter = true;
refFit = rf.getFit();
bestSpeed = speed;
bestEst = nextEst;
break;
}
speed *= shrinkage;
}
if (false && foundBetter) {
// In some tests, this did not help so it is not enabled.
// It might be worth testing more.
mxLog("trying larger step size");
retries = 3;
while (--retries > 0 && !isErrorRaised()){
speed *= 1.01;
Eigen::VectorXd nextEst = majorEst - speed * searchDir;
nextEst = nextEst.cwiseMax(rf.solLB).cwiseMin(rf.solUB);
rf.checkActiveBoxConstraints(nextEst);
int mode = 0;
double fit = rf.solFun(nextEst.data(), &mode);
if (fit < refFit) {
foundBetter = true;
refFit = rf.getFit();
bestSpeed = speed;
bestEst = nextEst;
}
}
}
if (!foundBetter) {
rf.informOut = INFORM_CONVERGED_OPTIMUM;
if(rf.verbose >= 2) mxLog("After %i iterations, cannot find better estimation along the gradient direction", iter);
break;
}
if (rf.verbose >= 2) mxLog("major fit %f bestSpeed %g", refFit, bestSpeed);
majorEst = bestEst;
priorSpeed = bestSpeed * 1.1;
}
rf.est = majorEst;
if ((rf.grad.array().abs() > 0.1).any()) {
rf.informOut = INFORM_NOT_AT_OPTIMUM;
}
if (iter >= maxIter - 1) {
rf.informOut = INFORM_ITERATION_LIMIT;
if(rf.verbose >= 2) mxLog("Maximum iteration achieved!");
}
if(rf.verbose >= 1) mxLog("Status code : %i", rf.informOut);
}
void omxInvokeNLOPT(double *est, GradientOptimizerContext &goc)
{
goc.optName = "SLSQP";
goc.setupSimpleBounds();
goc.useGradient = true;
FitContext *fc = goc.fc;
int oldWanted = fc->wanted;
fc->wanted = 0;
omxState *globalState = fc->state;
nlopt_opt opt = nlopt_create(NLOPT_LD_SLSQP, fc->numParam);
goc.extraData = opt;
//local_opt = nlopt_create(NLOPT_LD_SLSQP, n); // Subsidiary algorithm
//nlopt_set_local_optimizer(opt, local_opt);
nlopt_set_lower_bounds(opt, goc.solLB.data());
nlopt_set_upper_bounds(opt, goc.solUB.data());
int eq, ieq;
globalState->countNonlinearConstraints(eq, ieq);
if (fc->CI) {
nlopt_set_xtol_rel(opt, 5e-3);
std::vector<double> tol(fc->numParam, std::numeric_limits<double>::epsilon());
nlopt_set_xtol_abs(opt, tol.data());
} else {
// The *2 is there to roughly equate accuracy with NPSOL.
nlopt_set_ftol_rel(opt, goc.ControlTolerance * 2);
nlopt_set_ftol_abs(opt, std::numeric_limits<double>::epsilon());
}
nlopt_set_min_objective(opt, SLSQP::nloptObjectiveFunction, &goc);
double feasibilityTolerance = Global->feasibilityTolerance;
SLSQP::context ctx(goc);
if (eq + ieq) {
ctx.origeq = eq;
if (ieq > 0){
goc.inequality.resize(ieq);
std::vector<double> tol(ieq, feasibilityTolerance);
nlopt_add_inequality_mconstraint(opt, ieq, SLSQP::nloptInequalityFunction, &goc, tol.data());
}
if (eq > 0){
goc.equality.resize(eq);
std::vector<double> tol(eq, feasibilityTolerance);
nlopt_add_equality_mconstraint(opt, eq, SLSQP::nloptEqualityFunction, &ctx, tol.data());
}
}
int priorIterations = fc->iterations;
int code = nlopt_optimize(opt, est, &fc->fit);
if (ctx.eqredundent) {
nlopt_remove_equality_constraints(opt);
eq -= ctx.eqredundent;
std::vector<double> tol(eq, feasibilityTolerance);
nlopt_add_equality_mconstraint(opt, eq, SLSQP::nloptEqualityFunction, &ctx, tol.data());
code = nlopt_optimize(opt, est, &fc->fit);
}
if (goc.verbose >= 2) mxLog("nlopt_optimize returned %d", code);
nlopt_destroy(opt);
fc->wanted = oldWanted;
if (code == NLOPT_INVALID_ARGS) {
Rf_error("NLOPT invoked with invalid arguments");
} else if (code == NLOPT_OUT_OF_MEMORY) {
Rf_error("NLOPT ran out of memory");
} else if (code == NLOPT_FORCED_STOP) {
if (fc->iterations - priorIterations <= 1) {
goc.informOut = INFORM_STARTING_VALUES_INFEASIBLE;
} else {
goc.informOut = INFORM_ITERATION_LIMIT;
}
} else if (code == NLOPT_ROUNDOFF_LIMITED) {
if (fc->iterations - priorIterations <= 2) {
Rf_error("%s: Failed due to singular matrix E or C in LSQ subproblem or "
"rank-deficient equality constraint subproblem or "
"positive directional derivative in line search", goc.optName);
} else {
goc.informOut = INFORM_NOT_AT_OPTIMUM; // is this correct? TODO
}
} else if (code < 0) {
Rf_error("NLOPT fatal error %d", code);
} else if (code == NLOPT_MAXEVAL_REACHED) {
goc.informOut = INFORM_ITERATION_LIMIT;
} else {
goc.informOut = INFORM_CONVERGED_OPTIMUM;
}
}
