num

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Published: Sep 4, 2021 License: BSD-3-Clause Imports: 8 Imported by: 23

README

Gosl. num. Fundamental numerical methods

PkgGoDev

This package implements essential numerical methods such as for root finding, numerical quadrature, numerical differentiation, and solution of simple nonlinear problems.

While the supackage num/qpck provides advanced quadrature schemes (by wrapping Quadpack), this package implements few (simpler) methods to compute numerical integrals. Here, there are two kinds of algorithms: (1) basic methods for discrete data; and (2) using refinment for integrating general functions.

Example: Using Brent's method:

Find the root of

    y(x) = x³ - 0.165 x² + 3.993e-4

within [0, 0.11]. See figure below. Note: we have to make sure that the root is bounded otherwise Brent's method doesn't work.

	// y(x) function
	yx := func(x float64) float64 {
		return math.Pow(x, 3.0) - 0.165*math.Pow(x, 2.0) + 3.993e-4
	}

	// range: be sure to enclose root
	xa, xb := 0.0, 0.11

	// initialise solver
	solver := num.NewBrent(yx, nil)

	// solve
	xo := solver.Root(xa, xb)

	// output
	yo := yx(xo)
	io.Pf("x      = %v\n", xo)
	io.Pf("f(x)   = %v\n", yo)
	io.Pf("nfeval = %v\n", solver.NumFeval)
	io.Pf("niter. = %v\n", solver.NumIter)

Output of Brent's solution:

  it                      x                   f(x)                    err
                                                                  1.0e-14
   0  1.100000000000000e-01 -2.662000000000001e-04  5.500000000000000e-02
   1  6.600000000000000e-02 -3.194400000000011e-05  3.300000000000000e-02
   2  6.044444444444443e-02  1.730305075445823e-05  2.777777777777785e-03
   3  6.239640011030302e-02 -1.676981032316081e-07  9.759778329292944e-04
   4  6.237766369176578e-02 -7.323468182796403e-10  9.666096236606754e-04
   5  6.237758151338346e-02  3.262039076357137e-15  4.108919116063703e-08
   6  6.237758151374950e-02  0.000000000000000e+00  4.108900814037142e-08

x      = 0.0623775815137495
f(x)   = 0
nfeval = 8
niter. = 6

Example: Using Newton's method:

Same problem as before.

	// Function: y(x) = fx[0] with x = xvec[0]
	fcn := func(fx, xvec la.Vector) {
		x := xvec[0]
		fx[0] = math.Pow(x, 3.0) - 0.165*math.Pow(x, 2.0) + 3.993e-4
	}

	// Jacobian: dfdx(x) function
	Jfcn := func(dfdx *la.Matrix, x la.Vector) {
		dfdx.Set(0, 0, 3.0*x[0]*x[0]-2.0*0.165*x[0])
		return
	}

	// trial solution
	xguess := 0.03

	// initialise solver
	neq := 1      // number of equations
	useDn := true // use dense Jacobian
	numJ := false // numerical Jacobian
	var o num.NlSolver
	o.Init(neq, fcn, nil, Jfcn, useDn, numJ, nil)

	// solve
	xvec := []float64{xguess}
	o.Solve(xvec, false)

	// output
	fx := []float64{123}
	fcn(fx, xvec)
	xo, yo := xvec[0], fx[0]
	io.Pf("x      = %v\n", xo)
	io.Pf("f(x)   = %v\n", yo)
	io.Pf("nfeval = %v\n", o.NFeval)
	io.Pf("niter. = %v\n", o.It)

Output of NlSolver:

  it                    Ldx                 fx_max
                  (1.0e-04)              (1.0e-09)
   0  0.000000000000000e+00  2.778000000000000e-04
   1  3.745954692556634e+06  5.421253067129628e-05
   2  6.176571448942142e+05  1.391803634400563e-06
   2  1.515117884960284e+04  5.314115983194589e-10
. . . converged with fx_max. nit=2, nFeval=4, nJeval=3

x      = 0.062377521883073835
f(x)   = 5.314115983194589e-10
nfeval = 4
niter. = 2

Example: Quadrature with discrete data

source code

Example: Quadrature with methods using refinement

source code

Example: numerical differentiation

Check first and second derivative of y(x) = sin(x)

	// define function and derivative function
	yFcn := func(x float64) float64 { return math.Sin(x) }
	dydxFcn := func(x float64) float64 { return math.Cos(x) }
	d2ydx2Fcn := func(x float64) float64 { return -math.Sin(x) }

	// run test for 11 points
	X := utl.LinSpace(0, 2*math.Pi, 11)
	io.Pf("          %8s %23s %23s %23s\n", "x", "analytical", "numerical", "error")
	for _, x := range X {

		// analytical derivatives
		dydxAna := dydxFcn(x)
		d2ydx2Ana := d2ydx2Fcn(x)

		// numerical derivative: dydx
		dydxNum := num.DerivCen5(x, 1e-3, func(t float64) float64 {
			return yFcn(t)
		})

		// numerical derivative d2ydx2
		d2ydx2Num := num.DerivCen5(x, 1e-3, func(t float64) float64 {
			return dydxFcn(t)
		})

		// check
		chk.PrintAnaNum(io.Sf("dy/dx   @ %.6f", x), 1e-10, dydxAna, dydxNum, true)
		chk.PrintAnaNum(io.Sf("d²y/dx² @ %.6f", x), 1e-10, d2ydx2Ana, d2ydx2Num, true)
	}

Examples: nonlinear problems

Find x0 and x1 such that f0 and f1 are zero, with:

f0(x0,x1) = 2.0*x0 - x1 - exp(-x0)
f1(x0,x1) = -x0 + 2.0*x1 - exp(-x1)

source code

Using analytical (sparse) Jacobian matrix

Output:

  it                    Ldx                 fx_max
                  (1.0e-05)              (1.0e-15)
   0  0.000000000000000e+00  4.993262053000914e+00
   1  8.266404824090484e+09  9.204814001140181e-01
   2  7.824673760247719e+08  9.107574803964624e-02
   3  9.482829646746747e+07  9.541527544986161e-04
   4  1.014523823737919e+06  1.051153347697564e-07
   5  1.117908116077260e+02  1.221245327087672e-15
   5  1.298802024636321e-06  1.110223024625157e-16
. . . converged with fx_max. nit=5, nFeval=12, nJeval=6
x    = [0.5671432904097838 0.5671432904097838]  expected = [0.5671 0.5671]
f(x) = [-1.1102230246251565e-16 -1.1102230246251565e-16]
Using numerical Jacobian matrix

Output:

  it                    Ldx                 fx_max
                  (1.0e-05)              (1.0e-15)
   0  0.000000000000000e+00  4.993262053000914e+00
   1  8.266404846831476e+09  9.204814268661379e-01
   2  7.824673975180904e+08  9.107574923794759e-02
   3  9.482829862677714e+07  9.541518971115659e-04
   4  1.014522914054942e+06  1.051134990159852e-07
   5  1.117888589104628e+02  1.554312234475219e-15
   5  1.653020764599351e-06  1.110223024625157e-16
. . . converged with fx_max. nit=5, nFeval=24, nJeval=6
xx    = [0.5671432904097838 0.5671432904097838]  expected = [0.5671 0.5671]
f(xx) = [-1.1102230246251565e-16 -1.1102230246251565e-16]
Using analytical dense Jacobian matrix

Just replace Jfcn with

JfcnD := func(dfdx [][]float64, x []float64) error {
    dfdx[0][0] = 2.0+math.Exp(-x[0])
    dfdx[0][1] = -1.0
    dfdx[1][0] = -1.0
    dfdx[1][1] = 2.0+math.Exp(-x[1])
    return nil
}

API

Please see the documentation here

Documentation

Overview

Package num implements fundamental numerical methods such as numerical derivative and quadrature, root finding solvers (Brent's and Newton's methods), among others.

Index

Constants

This section is empty.

Variables

View Source
var (
	MACHEPS = math.Nextafter(1, 2) - 1.0 // smallest number satisfying 1 + EPS > 1
)

constants

Functions

func CompareJac

func CompareJac(tst *testing.T, ffcn fun.Vv, Jfcn fun.Tv, x []float64, tol float64)

CompareJac compares Jacobian matrix (e.g. for testing)

func CompareJacDense added in v1.1.0

func CompareJacDense(tst *testing.T, ffcn fun.Vv, Jfcn fun.Mv, x []float64, tol float64)

CompareJacDense compares Jacobian matrix (e.g. for testing) in dense format

func DerivBwd4

func DerivBwd4(x, h float64, f fun.Ss) (res float64)

DerivBwd4 approximates the derivative df/dx using backward differences with 4 points.

func DerivCen5

func DerivCen5(x, h float64, f fun.Ss) (res float64)

DerivCen5 approximates the derivative df/dx using central differences with 5 points.

func DerivFwd4

func DerivFwd4(x, h float64, f fun.Ss) (res float64)

DerivFwd4 approximates the derivative df/dx using forward differences with 4 points.

func EqCubicSolveReal

func EqCubicSolveReal(a, b, c float64) (x1, x2, x3 float64, nx int)

EqCubicSolveReal solves a cubic equation, ignoring the complex answers.

The equation is specified by:
 x³ + a x² + b x + c = 0
Notes:
 1) every cubic equation with real coefficients has at least one solution
    x among the real numbers
 2) from Numerical Recipes 2007, page 228
Output:
 x[i] -- roots
 nx   -- number of real roots: 1, 2 or 3

func GaussJacobiXW

func GaussJacobiXW(alf, bet float64, n int) (x, w []float64)

GaussJacobiXW computes positions (xi) and weights (wi) to perform Gauss-Jacobi integrations. The largest abscissa is returned in x[0], the smallest in x[n-1]. The interval of integration is x ϵ [-1, 1]

Input:
  alp -- coefficient of the Jacobi polynomial
  bet -- coefficient of the Jacobi polynomial
  n   -- number of points for quadrature formula
Reference:
[1] Press WH, Teukolsky SA, Vetterling WT, Fnannery BP (2007) Numerical Recipes: The Art of
    Scientific Computing. Third Edition. Cambridge University Press. 1235p.

func GaussLegendreXW

func GaussLegendreXW(x1, x2 float64, n int) (x, w []float64)

GaussLegendreXW computes positions (xi) and weights (wi) to perform Gauss-Legendre integrations

Input:
  x1 -- lower limit of integration
  x2 -- upper limit of integration
  n  -- number of points for quadrature formula
Reference:
[1] Press WH, Teukolsky SA, Vetterling WT, Fnannery BP (2007) Numerical Recipes: The Art of
    Scientific Computing. Third Edition. Cambridge University Press. 1235p.

func Jacobian

func Jacobian(J *la.Triplet, ffcn fun.Vv, x, fx, w []float64)

Jacobian computes Jacobian (sparse) matrix

    Calculates (with N=n-1):
        df0dx0, df0dx1, df0dx2, ... df0dxN
        df1dx0, df1dx1, df1dx2, ... df1dxN
             . . . . . . . . . . . . .
        dfNdx0, dfNdx1, dfNdx2, ... dfNdxN
INPUT:
    ffcn : f(x) function
    x    : station where dfdx has to be calculated
    fx   : f @ x
    w    : workspace with size == n == len(x)
RETURNS:
    J : dfdx @ x [must be pre-allocated]

func LinFit added in v1.0.1

func LinFit(x, y []float64) (a, b float64)

LinFit computes linear fitting parameters. Errors on y-direction only

y(x) = a + b⋅x

See page 780 of [1]
Reference:
[1] Press WH, Teukolsky SA, Vetterling WT, Fnannery BP (2007) Numerical Recipes: The Art of
    Scientific Computing. Third Edition. Cambridge University Press. 1235p.

func LinFitSigma added in v1.0.1

func LinFitSigma(x, y []float64) (a, b, σa, σb, χ2 float64)

LinFitSigma computes linear fitting parameters and variances (σa,σb) in the estimates of a and b Errors on y-direction only

y(x) = a + b⋅x

See page 780 of [1]
Reference:
[1] Press WH, Teukolsky SA, Vetterling WT, Fnannery BP (2007) Numerical Recipes: The Art of
    Scientific Computing. Third Edition. Cambridge University Press. 1235p.

func LineSearch

func LineSearch(x, fx []float64, ffcn fun.Vv, dx, x0, dφdx0 []float64, φ0 float64, maxIt int, dxIsMdx bool) (nFeval int)

LineSearch finds a new point x along the direction dx, from x0, where the function has decreased sufficiently. The new function value is returned in fx

INPUT:
    ffcn    -- f(x) callback
    dx      -- direction vector
    x0      -- initial x
    dφdx0   -- initial dφdx0 = fx * dfdx
    φ0      -- initial φ = 0.5 * dot(fx,fx)
    maxIt   -- max number of iterations
    dxIsMdx -- whether dx is actually -dx ==> IMPORTANT: dx will then be changed dx := -dx

OUTPUT:
    x      -- updated x (along dx)
    fx     -- updated f(x)
    φ0     -- updated φ = 0.5 * dot(fx,fx)
    dx     -- changed to -dx if dx_is_mdx == true
    nFeval -- number of calls to f(x)

func QuadCs added in v1.0.1

func QuadCs(a, b, ω float64, useSin bool, fid int, f func(x float64) float64) (res float64)

QuadCs performs automatic integration (quadrature) using the cosine or sine weights QUADPACK routine AWOE (Automatic with weight, Oscillatory)

INPUT:
  a      -- lower limit of integration
  b      -- upper limit of integration
  ω      -- omega
  useSin -- use sin(ω⋅x) instead of cos(ω⋅x)
  fid    -- index of goroutine (to avoid race problems)
  f      -- function defining the integrand

OUTPUT:          b                                     b
          res = ∫  f(x) ⋅ cos(ω⋅x) dx     or    res = ∫ f(x) ⋅ sin(ω⋅x) dx
                a                                     a

func QuadDiscreteSimps2d

func QuadDiscreteSimps2d(Lx, Ly float64, f [][]float64) (V float64)

QuadDiscreteSimps2d approximates a double integral over the x-y plane with the elevation given by data points f[npts][npts]. Thus, the result is an estimate of the volume below the f[][] opints and the plane ortogonal to z @ x=0. The very simple Simpson's method is used here.

Lx -- total length of plane along x
Ly -- total length of plane along y
f  -- elevations f(x,y)

func QuadDiscreteSimpsonRF

func QuadDiscreteSimpsonRF(a, b float64, n int, f fun.Ss) (res float64)

QuadDiscreteSimpsonRF approximates the area below the discrete curve defined by [xa,xy] range and y function. Computations are carried out with the (very simple) Simpson method from xa to xb, with npts points

func QuadDiscreteTrapz2d

func QuadDiscreteTrapz2d(Lx, Ly float64, f [][]float64) (V float64)

QuadDiscreteTrapz2d approximates a double integral over the x-y plane with the elevation given by data points f[npts][npts]. Thus, the result is an estimate of the volume below the f[][] opints and the plane ortogonal to z @ x=0. The very simple trapezoidal method is used here.

Lx -- total length of plane along x
Ly -- total length of plane along y
f  -- elevations f(x,y)

func QuadDiscreteTrapzRF

func QuadDiscreteTrapzRF(xa, xb float64, npts int, y fun.Ss) (A float64)

QuadDiscreteTrapzRF approximates the area below the discrete curve defined by [xa,xy] range and y function. Computations are carried out with the (very simple) trapezoidal rule from xa to xb, with npts points

func QuadDiscreteTrapzXF

func QuadDiscreteTrapzXF(x []float64, y fun.Ss) (A float64)

QuadDiscreteTrapzXF approximates the area below the discrete curve defined by x points and y function. Computations are carried out with the (very simple) trapezoidal rule.

func QuadDiscreteTrapzXY

func QuadDiscreteTrapzXY(x, y []float64) (A float64)

QuadDiscreteTrapzXY approximates the area below the discrete curve defined by x and y points. Computations are carried out with the trapezoidal rule.

func QuadExpIx added in v1.0.1

func QuadExpIx(a, b, m float64, fid int, f func(x float64) float64) (res complex128)

QuadExpIx approximates the integral of f(x) ⋅ exp(i⋅m⋅x) with i = √-1

INPUT:
  a      -- lower limit of integration
  b      -- upper limit of integration
  m      -- coefficient of x
  fid    -- index of goroutine (to avoid race problems)
  f      -- function defining the integrand

OUTPUT:        b                           b                           b
        res = ∫  f(x) ⋅ exp(i⋅m⋅x) dx   = ∫  f(x) ⋅ cos(m⋅x) dx + i ⋅ ∫  f(x) ⋅ sin(m⋅x) dx
              a                           a                           a

func QuadGaussL10

func QuadGaussL10(a, b float64, f fun.Ss) (res float64)

QuadGaussL10 approximates the integral of the function f(x) between a and b, by ten-point Gauss-Legendre integration. The function is evaluated exactly ten times at interior points in the range of integration. See page 180 of [1].

Reference:
[1] Press WH, Teukolsky SA, Vetterling WT, Fnannery BP (2007) Numerical Recipes: The Art of
    Scientific Computing. Third Edition. Cambridge University Press. 1235p.

func QuadGen added in v1.0.1

func QuadGen(a, b float64, fid int, f func(x float64) float64) (res float64)

QuadGen performs automatic integration (quadrature) using the general-purpose QUADPACK routine AGSE (Automatic, general-purpose, end-points singularities).

INPUT:
  a      -- lower limit of integration
  b      -- upper limit of integration
  fid    -- index of goroutine (to avoid race problems)
  f      -- function defining the integrand

OUTPUT:          b
          res = ∫  f(x) dx
                a

func SecondDerivCen3 added in v1.1.0

func SecondDerivCen3(x, h float64, f fun.Ss) float64

SecondDerivCen3 approximates the second derivative d²f/dx² using central differences with 3 points

func SecondDerivCen5 added in v1.1.0

func SecondDerivCen5(x, h float64, f fun.Ss) float64

SecondDerivCen5 approximates the second derivative d²f/dx² using central differences with 5 points

func SecondDerivMixedO2 added in v1.1.0

func SecondDerivMixedO2(x, y, h float64, f fun.Sss) float64

SecondDerivMixedO2 approximates ∂²f/∂x∂y @ x={x,y} using O(h²) formula

func SecondDerivMixedO4v1 added in v1.1.0

func SecondDerivMixedO4v1(x, y, h float64, f fun.Sss) float64

SecondDerivMixedO4v1 approximates ∂²f/∂x∂y @ x={x,y} using O(h⁴) formula from http://www.holoborodko.com/pavel/numerical-methods/numerical-derivative/central-differences

func SecondDerivMixedO4v2 added in v1.1.0

func SecondDerivMixedO4v2(x, y, h float64, f fun.Sss) float64

SecondDerivMixedO4v2 approximates ∂²f/∂x∂y @ x={x,y} using O(h⁴) formula from http://www.holoborodko.com/pavel/numerical-methods/numerical-derivative/central-differences

Types

type Bracket added in v1.1.0

type Bracket struct {

	// configuration
	MaxIt   int  // max iterations
	Verbose bool // show messages

	// statistics
	NumFeval int // number of calls to Ffcn (function evaluations)
	NumIter  int // number of iterations from last call to Solve
	// contains filtered or unexported fields
}

Bracket implements routines to bracket roots or optima

A root of a function is known to be bracketed by a pair of points, a and b,
when the function has opposite sign at those two points.

A minimum is known to be bracketed only when there is a triplet of points,
a < b < c (or c < b < a), such that f(b) is less than both f(a) and f(c)

REFERENCES:
[1] Press WH, Teukolsky SA, Vetterling WT, Fnannery BP (2007) Numerical Recipes:
    The Art of Scientific Computing. Third Edition. Cambridge University Press. 1235p.

func NewBracket added in v1.1.0

func NewBracket(ffcn fun.Ss) (o *Bracket)

NewBracket returns a new bracket-er object

func (*Bracket) Min added in v1.1.0

func (o *Bracket) Min(a0, b0 float64) (a, b, c, fa, fb, fc float64)

Min brackets minimum

Given a function and given distinct initial points a0 and b0, search in the downhill direction
(defined by the function as evaluated at the initial points) and return new points a, b, c
that bracket a minimum of the function.

Returns also the function values at the three points, fa, fb, and fc

type Brent

type Brent struct {

	// configuration
	MaxIt   int     // max iterations
	Tol     float64 // tolerance
	Verbose bool    // show messages

	// statistics
	NumFeval int // number of calls to Ffcn (function evaluations)
	NumJeval int // number of calls to Jfcn (Jacobian/derivatives)
	NumIter  int // number of iterations from last call to Solve

	Jfcn fun.Ss // Jfcn(x) = dy/dx [optional / may be nil]
	// contains filtered or unexported fields
}

Brent implements Brent's method for finding the roots of an equation

func NewBrent added in v1.1.0

func NewBrent(ffcn, Jfcn fun.Ss) (o *Brent)

NewBrent returns a new Brent structure

ffcn -- function f(x)
Jfcn -- derivative df(x)/dx [optinal / may be nil]

func (*Brent) Min

func (o *Brent) Min(xa, xb float64) (xAtMin float64)

Min finds the minimum of f(x) in [xa, xb]

Based on ZEROIN C math library: http://www.netlib.org/c/
By: Oleg Keselyov <oleg@ponder.csci.unt.edu, oleg@unt.edu> May 23, 1991

 G.Forsythe, M.Malcolm, C.Moler, Computer methods for mathematical
 computations. M., Mir, 1980, p.202 of the Russian edition

 The function makes use of the "gold section" procedure combined with
 the parabolic interpolation.
 At every step program operates three abscissae - x,v, and w.
 x - the last and the best approximation to the minimum location,
     i.e. f(x) <= f(a) or/and f(x) <= f(b)
     (if the function f has a local minimum in (a,b), then the both
     conditions are fulfilled after one or two steps).
 v,w are previous approximations to the minimum location. They may
 coincide with a, b, or x (although the algorithm tries to make all
 u, v, and w distinct). Points x, v, and w are used to construct
 interpolating parabola whose minimum will be treated as a new
 approximation to the minimum location if the former falls within
 [a,b] and reduces the range enveloping minimum more efficient than
 the gold section procedure.
 When f(x) has a second derivative positive at the minimum location
 (not coinciding with a or b) the procedure converges superlinearly
 at a rate order about 1.324

 The function always obtains a local minimum which coincides with
 the global one only if a function under investigation being
 unimodular. If a function being examined possesses no local minimum
 within the given range, Fminbr returns 'a' (if f(a) < f(b)), otherwise
 it returns the right range boundary value b.

func (*Brent) MinUseD added in v1.1.0

func (o *Brent) MinUseD(xa, xb float64) (xAtMin float64)

MinUseD finds minimum and uses information about derivatives

Given a function and deriva funcd that computes a function and also its derivative function df, and
given a bracketing triplet of abscissas ax, bx, cx [such that bx is between ax and cx, and
f(bx) is less than both f(ax) and f(cx)], this routine isolates the minimum to a fractional
precision of about tol using a modification of Brent’s method that uses derivatives. The
abscissa of the minimum is returned as xAtMin, and the minimum function value is returned
as min, the returned function value.

REFERENCES:
[1] Press WH, Teukolsky SA, Vetterling WT, Fnannery BP (2007) Numerical Recipes:
    The Art of Scientific Computing. Third Edition. Cambridge University Press. 1235p.

func (*Brent) Root added in v1.1.0

func (o *Brent) Root(xa, xb float64) (res float64)

Root solves y(x) = 0 for x in [xa, xb] with f(xa) * f(xb) < 0

Based on ZEROIN C math library: http://www.netlib.org/c/
By: Oleg Keselyov <oleg@ponder.csci.unt.edu, oleg@unt.edu> May 23, 1991

 G.Forsythe, M.Malcolm, C.Moler, Computer methods for mathematical
 computations. M., Mir, 1980, p.180 of the Russian edition

 The function makes use of the bissection procedure combined with
 the linear or quadric inverse interpolation.
 At every step program operates on three abscissae - a, b, and c.
 b - the last and the best approximation to the root
 a - the last but one approximation
 c - the last but one or even earlier approximation than a that
     1) |f(b)| <= |f(c)|
     2) f(b) and f(c) have opposite signs, i.e. b and c confine
        the root
 At every step Zeroin selects one of the two new approximations, the
 former being obtained by the bissection procedure and the latter
 resulting in the interpolation (if a,b, and c are all different
 the quadric interpolation is utilized, otherwise the linear one).
 If the latter (i.e. obtained by the interpolation) point is
 reasonable (i.e. lies within the current interval [b,c] not being
 too close to the boundaries) it is accepted. The bissection result
 is used in the other case. Therefore, the range of uncertainty is
 ensured to be reduced at least by the factor 1.6

type ElementarySimpson

type ElementarySimpson struct {
	// contains filtered or unexported fields
}

ElementarySimpson structure implements the Simpson's method for quadrature with refinement.

func (*ElementarySimpson) Init

func (o *ElementarySimpson) Init(f fun.Ss, a, b, eps float64)

Init initialises Simp structure

func (*ElementarySimpson) Integrate

func (o *ElementarySimpson) Integrate() (res float64)

Integrate performs the numerical integration

func (*ElementarySimpson) Next

func (o *ElementarySimpson) Next() (res float64)

Next returns the nth stage of refinement of the extended trapezoidal rule. On the first call (n=1), R b the routine returns the crudest estimate of a f .x/dx. Subsequent calls set n=2,3,... and improve the accuracy by adding 2 n-2 additional interior points.

type ElementaryTrapz

type ElementaryTrapz struct {
	// contains filtered or unexported fields
}

ElementaryTrapz structure is used for the trapezoidal integration rule with refinement.

func (*ElementaryTrapz) Init

func (o *ElementaryTrapz) Init(f fun.Ss, a, b, eps float64)

Init initialises Trap structure

func (*ElementaryTrapz) Integrate

func (o *ElementaryTrapz) Integrate() (res float64)

Integrate performs the numerical integration

func (*ElementaryTrapz) Next

func (o *ElementaryTrapz) Next() (res float64)

Next returns the nth stage of refinement of the extended trapezoidal rule. On the first call (n=1), R b the routine returns the crudest estimate of a f .x/dx. Subsequent calls set n=2,3,... and improve the accuracy by adding 2 n-2 additional interior points.

type LineSolver added in v1.1.0

type LineSolver struct {

	// configuration
	UseDeriv bool // use Jacobian function [default = true if Jfcn is provided]

	Jfcn fun.Vv // vector function of vector: {J} = df/d{x} @ {x} [optional / may be nil]

	// Stat
	NumFeval int // number of function evalutions
	NumJeval int // number of Jacobian evaluations
	// contains filtered or unexported fields
}

LineSolver finds the scalar λ that zeroes or minimizes f(x+λ⋅n)

func NewLineSolver added in v1.1.0

func NewLineSolver(size int, ffcn fun.Sv, Jfcn fun.Vv) (o *LineSolver)

NewLineSolver returns a new LineSolver object

size -- length(x)
ffcn -- scalar function of vector: y = f({x})
Jfcn -- vector function of vector: {J} = df/d{x} @ {x} [optional / may be nil]

func (*LineSolver) G added in v1.1.0

func (o *LineSolver) G(λ float64) float64

G implements g(λ) := f({y}(λ)) where {y}(λ) := {x} + λ⋅{n}

func (*LineSolver) H added in v1.1.0

func (o *LineSolver) H(λ float64) float64

H implements h(λ) = dg/dλ = df/d{y} ⋅ d{y}/dλ where {y} == {x} + λ⋅{n}

func (*LineSolver) Min added in v1.1.0

func (o *LineSolver) Min(x, n la.Vector) (λ float64)

Min finds the scalar λ that minimizes f(x+λ⋅n)

func (*LineSolver) MinUpdateX added in v1.1.0

func (o *LineSolver) MinUpdateX(x, n la.Vector) (λ, fmin float64)

MinUpdateX finds the scalar λ that minimizes f(x+λ⋅n), updates x and returns fmin = f({x})

Input:
  x -- initial point
  n -- direction
Output:
  λ -- scale parameter
  x -- x @ minimum
  fmin -- f({x})

func (*LineSolver) Root added in v1.1.0

func (o *LineSolver) Root(x, n la.Vector) (λ float64)

Root finds the scalar λ that zeroes f(x+λ⋅n)

func (*LineSolver) Set added in v1.1.0

func (o *LineSolver) Set(x, n la.Vector)

Set sets x and n vectors as required by G(λ) and H(λ) functions

type NlSolver

type NlSolver struct {

	// stats
	Niter  int // number of iterations from the last call to Solve
	Nfeval int // number of calls to Ffcn (function evaluations)
	Njeval int // number of calls to Jfcn (Jacobian evaluations)
	// contains filtered or unexported fields
}

NlSolver implements a solver to nonlinear systems of equations

References:
 [1] G.Forsythe, M.Malcolm, C.Moler, Computer methods for mathematical
     computations. M., Mir, 1980, p.180 of the Russian edition

func NewNlSolver added in v1.2.1

func NewNlSolver(neq int, F fun.Vv) (o *NlSolver)

NewNlSolver creates a new NlSolver F is the f(x) function f:vector, x:vector Will use numerical Jacobian (with sparse solver) by default

func (*NlSolver) CheckJ

func (o *NlSolver) CheckJ(x []float64, tol float64, verbose bool) (cnd float64)

CheckJ check Jacobian matrix

Ouptut: cnd -- condition number (with Frobenius norm)

func (*NlSolver) Free

func (o *NlSolver) Free()

Free frees memory

func (*NlSolver) SetJacobianFunction added in v1.2.1

func (o *NlSolver) SetJacobianFunction(Jsparse fun.Tv, Jdense fun.Mv)

SetJacobianFunction sets function to compute the Jacobian (dense or sparse) One of sparse [recommended] or dense must be given. If both sparse and dense functions are given, the sparse will be used. With Jdense, matrix inversion is used (not very efficient. use for small systems)

func (*NlSolver) Solve

func (o *NlSolver) Solve(x []float64)

Solve solves non-linear problem f(x) == 0 x -- trial x "near" the solution; otherwise it may not converge

type NlSolverConfig added in v1.2.1

type NlSolverConfig struct {
	// input
	Verbose          bool // show messages
	ConstantJacobian bool // constant Jacobian (Modified Newton's method)
	LineSearch       bool // use line search
	LineSearchMaxIt  int  // line search maximum iterations
	MaxIterations    int  // Newton's method maximum iterations
	EnforceConvRate  bool // check and enforce convergence rate

	// function to be called during each output
	OutCallback func(x []float64) // output callback function

	// configurations for linear solver
	LinSolConfig *la.SparseConfig // configurations for sparse linear solver
	// contains filtered or unexported fields
}

NlSolverConfig holds the configuration input for NlSolver

func NewNlSolverConfig added in v1.2.1

func NewNlSolverConfig() (o *NlSolverConfig)

NewNlSolverConfig creates a new NlSolverConfig Default values:

CteJac      = false
LinSearch   = false
LinSchMaxIt = 20
MaxIt       = 20
ChkConv     = false
Atol        = 1e-8
Rtol        = 1e-8
Ftol        = 1e-9

func (*NlSolverConfig) SetTolerances added in v1.2.1

func (o *NlSolverConfig) SetTolerances(atol, rtol, ftol float64)

SetTolerances sets all tolerances

type QuadElementary

type QuadElementary interface {
	Init(f fun.Ss, a, b, eps float64) // The constructor takes as inputs f, the function or functor to be integrated between limits a and b, also input.
	Integrate() float64               // Returns the integral for the specified input data
}

QuadElementary defines the interface for elementary quadrature algorithms with refinement.

Directories

Path Synopsis
Package qpck wraps the QUADPACK library for numerical integration
Package qpck wraps the QUADPACK library for numerical integration

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