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CMA stop conditions #401

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CMA stop conditions #401

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5950e89
Changes so code equals paper initializations. Added more termination …
beny-bee Jun 14, 2024
ac8adca
condition number was reversed
beny-bee Jun 14, 2024
e8f21e7
tolupsigma stopping implemented
beny-bee Jun 14, 2024
baf07d9
Implemented noeffectavis and noeffectcoord
beny-bee Jun 19, 2024
718d1c1
Implemented noeffectaxis and noeffectcoord
beny-bee Jun 19, 2024
c5f103b
Termination criteria
beny-bee Jul 29, 2024
3e96c67
fix CMA-ES example
beny-bee Jul 29, 2024
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83 changes: 76 additions & 7 deletions include/ensmallen_bits/cmaes/cmaes_impl.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -31,14 +31,18 @@ CMAES<SelectionPolicyType, TransformationPolicyType>::CMAES(const size_t lambda,
const size_t maxIterations,
const double tolerance,
const SelectionPolicyType& selectionPolicy,
double stepSizeIn) :
double stepSizeIn,
const double minObjective = std::numeric_limits<double>::lowest(),
const int maxFunctionEvaluations = std::numeric_limits<int>::max()):
lambda(lambda),
batchSize(batchSize),
maxIterations(maxIterations),
tolerance(tolerance),
selectionPolicy(selectionPolicy),
transformationPolicy(transformationPolicy),
stepSize(stepSizeIn)
stepSize(stepSizeIn),
minObjective(minObjective),
maxFunctionEvaluations(maxFunctionEvaluations)
{ /* Nothing to do. */ }

template<typename SelectionPolicyType, typename TransformationPolicyType>
Expand All @@ -49,13 +53,16 @@ CMAES<SelectionPolicyType, TransformationPolicyType>::CMAES(const size_t lambda,
const size_t maxIterations,
const double tolerance,
const SelectionPolicyType& selectionPolicy,
double stepSizeIn) :
double stepSizeIn,
const double minObjective = std::numeric_limits<double>::lowest(),
const int maxFunctionEvaluations = std::numeric_limits<int>::max()):
lambda(lambda),
batchSize(batchSize),
maxIterations(maxIterations),
tolerance(tolerance),
selectionPolicy(selectionPolicy),
stepSize(stepSizeIn)
stepSize(stepSizeIn),
minObjective(minObjective)
{
Warn << "This is a deprecated constructor and will be removed in a "
"future version of ensmallen" << std::endl;
Expand Down Expand Up @@ -89,9 +96,12 @@ typename MatType::elem_type CMAES<SelectionPolicyType,
// Find the number of functions to use.
const size_t numFunctions = function.NumFunctions();

// Initialize the counter for the number of function evaluations.
size_t numFunctionEvaluations = 0;

// Population size.
if (lambda == 0)
lambda = (4 + std::round(3 * std::log(iterate.n_elem))) * 10;
lambda = (4 + std::round(3 * std::log(iterate.n_elem)));

// Parent weights.
const size_t mu = std::round(lambda / 2);
Expand All @@ -109,7 +119,7 @@ typename MatType::elem_type CMAES<SelectionPolicyType,
else
sigma(0) = stepSize;

const double cs = (muEffective + 2) / (iterate.n_elem + muEffective + 5);
const double cs = (muEffective + 2) / (iterate.n_elem + muEffective + 3);
const double ds = 1 + cs + 2 * std::max(std::sqrt((muEffective - 1) /
(iterate.n_elem + 1)) - 1, 0.0);
const double enn = std::sqrt(iterate.n_elem) * (1.0 - 1.0 /
Expand Down Expand Up @@ -151,6 +161,7 @@ typename MatType::elem_type CMAES<SelectionPolicyType,
terminate |= Callback::Evaluate(*this, function, transformedIterate,
objective, callbacks...);
}
numFunctionEvaluations += numFunctions;

ElemType overallObjective = currentObjective;
ElemType lastObjective = std::numeric_limits<ElemType>::max();
Expand Down Expand Up @@ -186,6 +197,10 @@ typename MatType::elem_type CMAES<SelectionPolicyType,
size_t patience = 10 + (30 * iterate.n_elem / lambda) + 1;
size_t steps = 0;

// The min and max objective values in the range. For early termination.
double minObjectiveInPatience = std::numeric_limits<double>::infinity();
double maxObjectiveInPatience = -std::numeric_limits<double>::infinity();

for (size_t i = 1; (i != maxIterations) && !terminate; ++i)
{
// To keep track of where we are.
Expand Down Expand Up @@ -235,6 +250,8 @@ typename MatType::elem_type CMAES<SelectionPolicyType,
transformationPolicy.Transform(mPosition[idx1]), terminate,
callbacks...);

numFunctionEvaluations += lambda; // I think, not totally sure

// Update best parameters.
if (currentObjective < overallObjective)
{
Expand Down Expand Up @@ -274,6 +291,17 @@ typename MatType::elem_type CMAES<SelectionPolicyType,
return overallObjective;
}

// Terminate if sigma/sigma0 is above 10^4 * max(D)
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Suggested change
// Terminate if sigma/sigma0 is above 10^4 * max(D)
// Terminate if sigma/sigma0 is above 10^4 * max(D).

To be consistent with the style.

if (sigma(idx1) / sigma(0) > 1e4 * std::max(eigvalZero, eigval))
{
Info << "The step size ratio is too large; "
<< "terminating with failure." << std::endl;

iterate = transformationPolicy.Transform(iterate);
Callback::EndOptimization(*this, function, iterate, callbacks...);
return overallObjective;
}

// Update covariance matrix.
if ((psNorm / sqrt(1 - std::pow(1 - cs, 2 * i))) < h)
{
Expand Down Expand Up @@ -340,6 +368,9 @@ typename MatType::elem_type CMAES<SelectionPolicyType,
}
}

minObjectiveInPatience = std::min(minObjectiveInPatience, currentObjective);
maxObjectiveInPatience = std::max(maxObjectiveInPatience, currentObjective);

// Output current objective function.
Info << "CMA-ES: iteration " << i << ", objective " << overallObjective
<< "." << std::endl;
Expand All @@ -354,7 +385,8 @@ typename MatType::elem_type CMAES<SelectionPolicyType,
return overallObjective;
}

if (std::abs(lastObjective - overallObjective) < tolerance)
if ((std::abs(lastObjective - overallObjective) < tolerance) &&
(maxObjectiveInPatience - minObjectiveInPatience < tolerance))
{
if (steps > patience) {
Info << "CMA-ES: minimized within tolerance " << tolerance << "; "
Expand All @@ -366,9 +398,46 @@ typename MatType::elem_type CMAES<SelectionPolicyType,
}
}
else {
minObjectiveInPatience = std::numeric_limits<double>::infinity();
maxObjectiveInPatience = -std::numeric_limits<double>::infinity();
steps = 0;
}

// Other termination criteria.
// Terminate if the objective is less than the minimum objective.
if (overallObjective < minObjective)
{
Info << "CMA-ES: minimized below minObjective " << minObjective << "; "
<< "terminating optimization." << std::endl;

iterate = transformationPolicy.Transform(iterate);
Callback::EndOptimization(*this, function, iterate, callbacks...);
return overallObjective;
}

// Terminate if the maximum number of function evaluations has been reached.
if (numFunctionEvaluations >= maxFunctionEvaluations)
{
Info << "CMA-ES: maximum number of function evaluations ("
<< maxFunctionEvaluations << ") reached; "
<< "terminating optimization." << std::endl;

iterate = transformationPolicy.Transform(iterate);
Callback::EndOptimization(*this, function, iterate, callbacks...);
return overallObjective;
}

// Check if the condition number of the covariance matrix is too high.
if (eigval(eigval.n_elem - 1) / eigval(0) > 1e14) // TODO: Probably put this into a parameter
{
Info << "CMA-ES: covariance matrix condition number is too high; "
<< "terminating with failure." << std::endl;

iterate = transformationPolicy.Transform(iterate);
Callback::EndOptimization(*this, function, iterate, callbacks...);
return overallObjective;
}

steps++;

lastObjective = overallObjective;
Expand Down