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* diff/fd: implement Hessian finite difference, and code cleanups. This commit primarily adds the Hessian function for finding a finite difference approximation to the Hessian. At the same time, it combines duplicated functionality across the difference routines so that the preludes to all the difference routines look similar
193 lines
5.0 KiB
Go
193 lines
5.0 KiB
Go
// Copyright ©2017 The gonum Authors. All rights reserved.
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// Use of this source code is governed by a BSD-style
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// license that can be found in the LICENSE file.
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package fd
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import (
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"math"
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"sync"
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"gonum.org/v1/gonum/mat"
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)
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// Hessian approximates the Hessian matrix of the multivariate function f
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// at the location x. That is
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// H_{i,j} = ∂^2 f(x)/∂x_i ∂x_j
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// If dst is not nil, the resulting H will be stored in-place into dst
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// and returned, otherwise a new matrix will be allocated first. Finite difference
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// formula and other options are specified by settings. If settings is nil,
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// the Hessian will be estimated using the Forward formula and a default step size.
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//
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// Hessian panics if the size of dst and x is not equal, or if the derivative
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// order of the formula is not 1.
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func Hessian(dst *mat.SymDense, f func(x []float64) float64, x []float64, settings *Settings) *mat.SymDense {
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n := len(x)
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if dst == nil {
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dst = mat.NewSymDense(n, nil)
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} else {
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if n2 := dst.Symmetric(); n2 != n {
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panic("hessian: dst size mismatch")
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}
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for i := 0; i < n; i++ {
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for j := i; j < n; j++ {
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dst.SetSym(i, j, 0)
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}
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}
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}
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// Default settings.
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formula := Forward
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step := math.Sqrt(formula.Step) // Use the sqrt because taking derivatives of derivatives.
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var originValue float64
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var originKnown, concurrent bool
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// Use user settings if provided.
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if settings != nil {
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if !settings.Formula.isZero() {
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formula = settings.Formula
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step = math.Sqrt(formula.Step)
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checkFormula(formula)
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if formula.Derivative != 1 {
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panic(badDerivOrder)
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}
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}
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if settings.Step != 0 {
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if settings.Step < 0 {
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panic(negativeStep)
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}
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step = settings.Step
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}
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originKnown = settings.OriginKnown
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originValue = settings.OriginValue
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concurrent = settings.Concurrent
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}
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evals := n * (n + 1) / 2 * len(formula.Stencil) * len(formula.Stencil)
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for _, pt := range formula.Stencil {
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if pt.Loc == 0 {
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evals -= n * (n + 1) / 2
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break
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}
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}
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nWorkers := computeWorkers(concurrent, evals)
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if nWorkers == 1 {
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hessianSerial(dst, f, x, formula.Stencil, step, originKnown, originValue)
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return dst
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}
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hessianConcurrent(dst, nWorkers, evals, f, x, formula.Stencil, step, originKnown, originValue)
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return dst
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}
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func hessianSerial(dst *mat.SymDense, f func(x []float64) float64, x []float64, stencil []Point, step float64, originKnown bool, originValue float64) {
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n := len(x)
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xCopy := make([]float64, n)
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fo := func() float64 {
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copy(xCopy, x)
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return f(x)
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}
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is2 := 1 / (step * step)
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origin := getOrigin(originKnown, originValue, fo, stencil)
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for i := 0; i < n; i++ {
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for j := i; j < n; j++ {
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var hess float64
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for _, pti := range stencil {
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for _, ptj := range stencil {
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var v float64
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if pti.Loc == 0 && ptj.Loc == 0 {
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v = origin
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} else {
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// Copying the code anew has two benefits. First, it
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// avoids floating point issues where adding and then
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// subtracting the step don't return to the exact same
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// location. Secondly, it protects against the function
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// modifying the input data.
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copy(xCopy, x)
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xCopy[i] += pti.Loc * step
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xCopy[j] += ptj.Loc * step
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v = f(xCopy)
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}
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hess += v * pti.Coeff * ptj.Coeff * is2
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}
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}
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dst.SetSym(i, j, hess)
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}
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}
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}
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func hessianConcurrent(dst *mat.SymDense, nWorkers, evals int, f func(x []float64) float64, x []float64, stencil []Point, step float64, originKnown bool, originValue float64) {
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n := dst.Symmetric()
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type run struct {
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i, j int
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iIdx, jIdx int
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result float64
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}
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send := make(chan run, evals)
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ans := make(chan run)
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var originWG sync.WaitGroup
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hasOrigin := usesOrigin(stencil)
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if hasOrigin {
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originWG.Add(1)
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// Launch worker to compute the origin.
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go func() {
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defer originWG.Done()
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xCopy := make([]float64, len(x))
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copy(xCopy, x)
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originValue = f(xCopy)
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}()
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}
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var workerWG sync.WaitGroup
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// Launch workers.
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for i := 0; i < nWorkers; i++ {
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workerWG.Add(1)
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go func(send <-chan run, ans chan<- run) {
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defer workerWG.Done()
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xCopy := make([]float64, len(x))
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for r := range send {
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if stencil[r.iIdx].Loc == 0 && stencil[r.jIdx].Loc == 0 {
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originWG.Wait()
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r.result = originValue
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} else {
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// See hessianSerial for comment on the copy.
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copy(xCopy, x)
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xCopy[r.i] += stencil[r.iIdx].Loc * step
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xCopy[r.j] += stencil[r.jIdx].Loc * step
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r.result = f(xCopy)
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}
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ans <- r
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}
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}(send, ans)
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}
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// Launch the distributor, which sends all of runs.
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go func(send chan<- run) {
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for i := 0; i < n; i++ {
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for j := i; j < n; j++ {
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for iIdx := range stencil {
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for jIdx := range stencil {
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send <- run{
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i: i, j: j, iIdx: iIdx, jIdx: jIdx,
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}
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}
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}
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}
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}
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close(send)
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// Wait for all the workers to quit, then close the ans channel.
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workerWG.Wait()
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close(ans)
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}(send)
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is2 := 1 / (step * step)
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// Read in the results.
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for r := range ans {
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v := r.result * stencil[r.iIdx].Coeff * stencil[r.jIdx].Coeff * is2
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v += dst.At(r.i, r.j)
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dst.SetSym(r.i, r.j, v)
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}
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}
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