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optimize: Change Settings to allow InitialLocation (#497)

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* optimize: Change Settings to allow InitialLocation

This modifies Settings to allow specifying an initial location and properties of the function (value, gradient, etc.). This allows to work with local optimizers that are seeded with initial settings. This has two fields that must be specified, InitX and InitValues. Ideally this would only be one location, but the difficulty is that the default value of the function is 0. We either must require the user to specify it is set (in this case that InitValues is non-zero), or require the user to change the default value away if it is not set. The former seems much safer.
This commit is contained in:
Brendan Tracey
2018-06-06 09:11:04 -06:00
committed by GitHub
parent ee7a7204dd
commit d56ca496c1
5 changed files with 96 additions and 53 deletions
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56
optimize/local.go
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@@ -4,7 +4,11 @@
package optimize
import "math"
import (
"math"
"gonum.org/v1/gonum/floats"
)
// Local finds a local minimum of a minimization problem using a sequential
// algorithm. A maximization problem can be transformed into a minimization
@@ -62,6 +66,10 @@ func Local(p Problem, initX []float64, settings *Settings, method Method) (*Resu
if settings == nil {
settings = DefaultSettings()
}
// Check that the initial location matches the one in settings.
if settings.InitX != nil && !floats.Equal(settings.InitX, initX) {
panic("local: initX does not match settings x location")
}
lg := &localGlobal{
Method: method,
InitX: initX,
@@ -193,38 +201,24 @@ func (l *localGlobal) cleanup(operation chan<- GlobalTask, result <-chan GlobalT
func (l *localGlobal) getStartingLocation(operation chan<- GlobalTask, result <-chan GlobalTask, task GlobalTask) Operation {
copy(task.X, l.InitX)
if l.Settings.UseInitialData {
task.F = l.Settings.InitialValue
if task.Gradient != nil {
g := l.Settings.InitialGradient
if g == nil {
panic("optimize: initial gradient is nil")
}
if len(g) != l.dim {
panic("optimize: initial gradient size mismatch")
}
copy(task.Gradient, g)
}
if task.Hessian != nil {
h := l.Settings.InitialHessian
if h == nil {
panic("optimize: initial Hessian is nil")
}
if h.Symmetric() != l.dim {
panic("optimize: initial Hessian size mismatch")
}
task.Hessian.CopySym(h)
}
// Construct the operation by what is missing.
needs := l.Method.Needs()
initOp := task.Op
op := NoOperation
if initOp&FuncEvaluation == 0 {
op |= FuncEvaluation
}
if needs.Gradient && initOp&GradEvaluation == 0 {
op |= GradEvaluation
}
if needs.Hessian && initOp&HessEvaluation == 0 {
op |= HessEvaluation
}
if op == NoOperation {
return NoOperation
}
eval := FuncEvaluation
if task.Gradient != nil {
eval |= GradEvaluation
}
if task.Hessian != nil {
eval |= HessEvaluation
}
task.Op = eval
task.Op = op
operation <- task
task = <-result
return task.Op