Add Lapliacian and CrossLaplacian difference functions

diff/fd/crosslaplacian.go

@@ -0,0 +1,190 @@
+// Copyright ©2017 The gonum Authors. All rights reserved.
+// Use of this source code is governed by a BSD-style
+// license that can be found in the LICENSE file.
+
+package fd
+
+import (
+	"math"
+	"sync"
+)
+
+// CrossLaplacian computes a Laplacian-like quantity for a function of two vectors
+// at the locations x and y.
+// It computes
+//  ∇_y · ∇_x f(x,y) = \sum_i ∂^2 f(x,y)/∂x_i ∂y_i
+// The two input vector lengths must be the same.
+//
+// Finite difference formula and other options are specified by settings. If
+// settings is nil, LaplacianTwo will be estimated using the Forward formula and
+// a default step size.
+//
+// CrossLaplacian panics if the two input vectors are not the same length, or if
+// the derivative order of the formula is not 1.
+func CrossLaplacian(f func(x, y []float64) float64, x, y []float64, settings *Settings) float64 {
+	n := len(x)
+	if n == 0 {
+		panic("laplaciantwo: x has zero length")
+	}
+	if len(x) != len(y) {
+		panic("laplaciantwo: input vector length mismatch")
+	}
+
+	// Default settings.
+	formula := Forward
+	step := math.Sqrt(formula.Step) // Use the sqrt because taking derivatives of derivatives.
+	var originValue float64
+	var originKnown, concurrent bool
+
+	// Use user settings if provided.
+	if settings != nil {
+		if !settings.Formula.isZero() {
+			formula = settings.Formula
+			step = math.Sqrt(formula.Step)
+			checkFormula(formula)
+			if formula.Derivative != 1 {
+				panic(badDerivOrder)
+			}
+		}
+		if settings.Step != 0 {
+			if settings.Step < 0 {
+				panic(negativeStep)
+			}
+			step = settings.Step
+		}
+		originKnown = settings.OriginKnown
+		originValue = settings.OriginValue
+		concurrent = settings.Concurrent
+	}
+
+	evals := n * len(formula.Stencil) * len(formula.Stencil)
+	for _, pt := range formula.Stencil {
+		if pt.Loc == 0 {
+			evals -= n
+		}
+	}
+
+	nWorkers := computeWorkers(concurrent, evals)
+	if nWorkers == 1 {
+		return crossLaplacianSerial(f, x, y, formula.Stencil, step, originKnown, originValue)
+	} else {
+		return crossLaplacianConcurrent(nWorkers, evals, f, x, y, formula.Stencil, step, originKnown, originValue)
+	}
+	panic("fix")
+}
+
+func crossLaplacianSerial(f func(x, y []float64) float64, x, y []float64, stencil []Point, step float64, originKnown bool, originValue float64) float64 {
+	n := len(x)
+	xCopy := make([]float64, len(x))
+	yCopy := make([]float64, len(y))
+	fo := func() float64 {
+		// Copy x and y in case they are modified during the call.
+		copy(xCopy, x)
+		copy(yCopy, y)
+		return f(x, y)
+	}
+	origin := getOrigin(originKnown, originValue, fo, stencil)
+
+	is2 := 1 / (step * step)
+	var laplacian float64
+	for i := 0; i < n; i++ {
+		for _, pty := range stencil {
+			for _, ptx := range stencil {
+				var v float64
+				if ptx.Loc == 0 && pty.Loc == 0 {
+					v = origin
+				} else {
+					// Copying the data anew has two benefits. First, it
+					// avoids floating point issues where adding and then
+					// subtracting the step don't return to the exact same
+					// location. Secondly, it protects against the function
+					// modifying the input data.
+					copy(yCopy, y)
+					copy(xCopy, x)
+					yCopy[i] += pty.Loc * step
+					xCopy[i] += ptx.Loc * step
+					v = f(xCopy, yCopy)
+				}
+				laplacian += v * ptx.Coeff * pty.Coeff * is2
+			}
+		}
+	}
+	return laplacian
+}
+
+func crossLaplacianConcurrent(nWorkers, evals int, f func(x, y []float64) float64, x, y []float64, stencil []Point, step float64, originKnown bool, originValue float64) float64 {
+	n := len(x)
+	type run struct {
+		i          int
+		xIdx, yIdx int
+		result     float64
+	}
+
+	send := make(chan run, evals)
+	ans := make(chan run, evals)
+
+	var originWG sync.WaitGroup
+	hasOrigin := usesOrigin(stencil)
+	if hasOrigin {
+		originWG.Add(1)
+		// Launch worker to compute the origin.
+		go func() {
+			defer originWG.Done()
+			xCopy := make([]float64, len(x))
+			yCopy := make([]float64, len(y))
+			copy(xCopy, x)
+			copy(yCopy, y)
+			originValue = f(xCopy, yCopy)
+		}()
+	}
+
+	var workerWG sync.WaitGroup
+	// Launch workers.
+	for i := 0; i < nWorkers; i++ {
+		workerWG.Add(1)
+		go func(send <-chan run, ans chan<- run) {
+			defer workerWG.Done()
+			xCopy := make([]float64, len(x))
+			yCopy := make([]float64, len(y))
+			for r := range send {
+				if stencil[r.xIdx].Loc == 0 && stencil[r.yIdx].Loc == 0 {
+					originWG.Wait()
+					r.result = originValue
+				} else {
+					// See crossLaplacianSerial for comment on the copy.
+					copy(xCopy, x)
+					copy(yCopy, y)
+					xCopy[r.i] += stencil[r.xIdx].Loc * step
+					yCopy[r.i] += stencil[r.yIdx].Loc * step
+					r.result = f(xCopy, yCopy)
+				}
+				ans <- r
+			}
+		}(send, ans)
+	}
+
+	// Launch the distributor, which sends all of runs.
+	go func(send chan<- run) {
+		for i := 0; i < n; i++ {
+			for xIdx := range stencil {
+				for yIdx := range stencil {
+					send <- run{
+						i: i, xIdx: xIdx, yIdx: yIdx,
+					}
+				}
+			}
+		}
+		close(send)
+		// Wait for all the workers to quit, then close the ans channel.
+		workerWG.Wait()
+		close(ans)
+	}(send)
+
+	// Read in the results.
+	is2 := 1 / (step * step)
+	var laplacian float64
+	for r := range ans {
+		laplacian += r.result * stencil[r.xIdx].Coeff * stencil[r.yIdx].Coeff * is2
+	}
+	return laplacian
+}

diff/fd/crosslaplacian_test.go

@@ -0,0 +1,111 @@
+// Copyright ©2017 The gonum Authors. All rights reserved.
+// Use of this source code is governed by a BSD-style
+// license that can be found in the LICENSE file.
+
+package fd
+
+import (
+	"testing"
+
+	"gonum.org/v1/gonum/floats"
+	"gonum.org/v1/gonum/mat"
+)
+
+type CrossLaplacianTester interface {
+	Func(x, y []float64) float64
+	CrossLaplacian(x, y []float64) float64
+}
+
+type WrapperCL struct {
+	Tester HessianTester
+}
+
+func (WrapperCL) constructZ(x, y []float64) []float64 {
+	z := make([]float64, len(x)+len(y))
+	copy(z, x)
+	copy(z[len(x):], y)
+	return z
+}
+
+func (w WrapperCL) Func(x, y []float64) float64 {
+	z := w.constructZ(x, y)
+	return w.Tester.Func(z)
+}
+
+func (w WrapperCL) CrossLaplacian(x, y []float64) float64 {
+	z := w.constructZ(x, y)
+	hess := mat.NewSymDense(len(z), nil)
+	w.Tester.Hess(hess, z)
+	// The LaplacianTwo is the trace of the off-diagonal block of the Hessian.
+	var l float64
+	for i := 0; i < len(x); i++ {
+		l += hess.At(i, i+len(x))
+	}
+	return l
+}
+
+func TestCrossLaplacian(t *testing.T) {
+	for cas, test := range []struct {
+		l        CrossLaplacianTester
+		x, y     []float64
+		settings *Settings
+		tol      float64
+	}{
+		{
+			l:   WrapperCL{Watson{}},
+			x:   []float64{0.2, 0.3},
+			y:   []float64{0.1, 0.4},
+			tol: 1e-3,
+		},
+		{
+			l:   WrapperCL{Watson{}},
+			x:   []float64{2, 3, 1},
+			y:   []float64{1, 4, 1},
+			tol: 1e-3,
+		},
+		{
+			l:   WrapperCL{ConstFunc(6)},
+			x:   []float64{2, -3, 1},
+			y:   []float64{1, 4, -5},
+			tol: 1e-6,
+		},
+		{
+			l:   WrapperCL{LinearFunc{w: []float64{10, 6, -1, 5}, c: 5}},
+			x:   []float64{3, 1},
+			y:   []float64{8, 6},
+			tol: 1e-6,
+		},
+		{
+			l: WrapperCL{QuadFunc{
+				a: mat.NewSymDense(4, []float64{
+					10, 2, 1, 9,
+					2, 5, -3, 4,
+					1, -3, 6, 2,
+					9, 4, 2, -14,
+				}),
+				b: mat.NewVector(4, []float64{3, -2, -1, 4}),
+				c: 5,
+			}},
+			x:   []float64{-1.6, -3},
+			y:   []float64{1.8, 3.4},
+			tol: 1e-6,
+		},
+	} {
+		got := CrossLaplacian(test.l.Func, test.x, test.y, test.settings)
+		want := test.l.CrossLaplacian(test.x, test.y)
+		if !floats.EqualWithinAbsOrRel(got, want, test.tol, test.tol) {
+			t.Errorf("Cas %d: LaplacianTwo mismatch serial. got %v, want %v", cas, got, want)
+		}
+
+		// Test that concurrency works.
+		settings := test.settings
+		if settings == nil {
+			settings = &Settings{}
+		}
+		settings.Concurrent = true
+		got2 := CrossLaplacian(test.l.Func, test.x, test.y, settings)
+		if !floats.EqualWithinAbsOrRel(got, got2, 1e-6, 1e-6) {
+			t.Errorf("Cas %d: Laplacian mismatch. got %v, want %v", cas, got2, got)
+		}
+	}
+}

diff/fd/gradient.go

@@ -51,7 +51,8 @@ func Gradient(dst []float64, f func([]float64) float64, x []float64, settings *S
 	nWorkers := computeWorkers(concurrent, evals)
 
 	hasOrigin := usesOrigin(formula.Stencil)
-	xcopy := make([]float64, len(x)) // So that x is not modified during the call.
+	// Copy x in case it is modified during the call.
+	xcopy := make([]float64, len(x))
 	if hasOrigin && !originKnown {
 		copy(xcopy, x)
 		originValue = f(xcopy)
@@ -65,7 +66,7 @@ func Gradient(dst []float64, f func([]float64) float64, x []float64, settings *S
 					deriv += pt.Coeff * originValue
 					continue
 				}
-				// Copying the code anew has two benefits. First, it
+				// Copying the data anew has two benefits. First, it
 				// avoids floating point issues where adding and then
 				// subtracting the step don't return to the exact same
 				// location. Secondly, it protects against the function

diff/fd/hessian.go

@@ -84,6 +84,7 @@ func hessianSerial(dst *mat.SymDense, f func(x []float64) float64, x []float64,
 	n := len(x)
 	xCopy := make([]float64, n)
 	fo := func() float64 {
+		// Copy x in case it is modified during the call.
 		copy(xCopy, x)
 		return f(x)
 	}
@@ -98,7 +99,7 @@ func hessianSerial(dst *mat.SymDense, f func(x []float64) float64, x []float64,
 					if pti.Loc == 0 && ptj.Loc == 0 {
 						v = origin
 					} else {
-						// Copying the code anew has two benefits. First, it
+						// Copying the data anew has two benefits. First, it
 						// avoids floating point issues where adding and then
 						// subtracting the step don't return to the exact same
 						// location. Secondly, it protects against the function
@@ -125,7 +126,7 @@ func hessianConcurrent(dst *mat.SymDense, nWorkers, evals int, f func(x []float6
 	}
 
 	send := make(chan run, evals)
-	ans := make(chan run)
+	ans := make(chan run, evals)
 
 	var originWG sync.WaitGroup
 	hasOrigin := usesOrigin(stencil)