Package 'numDeriv' reference manual

Title:	Accurate Numerical Derivatives
Description:	Methods for calculating (usually) accurate numerical first and second order derivatives. Accurate calculations are done using 'Richardson''s' extrapolation or, when applicable, a complex step derivative is available. A simple difference method is also provided. Simple difference is (usually) less accurate but is much quicker than 'Richardson''s' extrapolation and provides a useful cross-check. Methods are provided for real scalar and vector valued functions.
Authors:	Paul Gilbert and Ravi Varadhan
Maintainer:	Paul Gilbert <[email protected]>
License:	GPL >= 2
Version:	2022.9-1
Built:	2024-11-08 19:16:04 UTC
Source:	https://github.com/r-forge/optimizer

Accurate Numerical Derivatives

Description

Calculate (accurate) numerical approximations to derivatives.

Details

The main functions are

grad	  to calculate the gradient (first derivative) of a scalar 
  	  real valued function (possibly applied to all elements 
  	  of a vector argument).

jacobian  to calculate the gradient of a real m-vector valued
  	  function with real n-vector argument.

hessian   to calculate the Hessian (second derivative) of a scalar 
  	  real valued function with real n-vector argument.

genD	  to calculate the gradient and second derivative of a
  	  real m-vector valued function with real n-vector 
	  argument.

Author(s)

Paul Gilbert, based on work by Xingqiao Liu, and Ravi Varadhan (who wrote complex-step derivative codes)

References

Linfield, G. R. and Penny, J. E. T. (1989) Microcomputers in Numerical Analysis. New York: Halsted Press.

Fornberg, B. and Sloan, D, M. (1994) “A review of pseudospectral methods for solving partial differential equations.” Acta Numerica, 3, 203-267.

Lyness, J. N. and Moler, C. B. (1967) “Numerical Differentiation of Analytic Functions.” SIAM Journal for Numerical Analysis, 4(2), 202-210.

Accurate Numerical Derivatives

Description

Calculate (accurate) numerical approximations to derivatives.

Details

See numDeriv-package ( in the help system use package?numDeriv or ?"numDeriv-package") for an overview.

Generate Bates and Watts D Matrix

Description

Generate a matrix of function derivative information.

Usage

    genD(func, x, method="Richardson",
                   method.args=list(), ...)
    ## Default S3 method:
genD(func, x, method="Richardson",
      method.args=list(), ...)
genD(func, x, method="Richardson",
                   method.args=list(), ...)
    ## Default S3 method:
genD(func, x, method="Richardson",
      method.args=list(), ...)

Arguments

`func`	a function for which the first (vector) argument is used as a parameter vector.
`x`	The parameter vector first argument to `func`.
`method`	one of `"Richardson"` or `"simple"` indicating the method to use for the aproximation.
`method.args`	arguments passed to method. See `grad`. (Arguments not specified remain with their default values.)
`...`	any additional arguments passed to `func`. WARNING: None of these should have names matching other arguments of this function.

Details

The derivatives are calculated numerically using Richardson improvement. Methods "simple" and "complex" are not supported in this function. The "Richardson" method calculates a numerical approximation of the first and second derivatives of func at the point x. For a scalar valued function these are the gradient vector and Hessian matrix. (See grad and hessian.) For a vector valued function the first derivative is the Jacobian matrix (see jacobian). For the Richardson method method.args=list(eps=1e-4, d=0.0001, zero.tol=sqrt(.Machine$double.eps/7e-7), r=4, v=2) is set as the default. See grad for more details on the Richardson's extrapolation parameters.

A simple approximation to the first order derivative with respect to $x_i$ is

$f'_{i}(x) = <f(x_{1},\dots,x_{i}+d,\dots,x_{n}) - f(x_{1},\dots,x_{i}-d,\dots,x_{n})>/(2*d)$

A simple approximation to the second order derivative with respect to $x_i$ is

$f''_{i}(x) = <f(x_{1},\dots,x_{i}+d,\dots,x_{n}) - 2 *f(x_{1},\dots,x_{n}) + f(x_{1},\dots,x_{i}-d,\dots,x_{n})>/(d^2)$

The second order derivative with respect to $x_i, x_j$ is

$f''_{i,j}(x) = <f(x_{1},\dots,x_{i}+d,\dots,x_{j}+d,\dots,x_{n}) - 2 *f(x_{1},\dots,x_{n}) +$

$f(x_{1},\dots,x_{i}-d,\dots,x_{j}-d,\dots,x_{n})>/(2*d^2) - (f''_{i}(x) + f''_{j}(x))/2$

Richardson's extrapolation is based on these formula with the d being reduced in the extrapolation iterations. In the code, d is scaled to accommodate parameters of different magnitudes.

genD does 1 + r (N^2 + N) evaluations of the function f, where N is the length of x.

Value

A list with elements as follows: D is a matrix of first and second order partial derivatives organized in the same manner as Bates and Watts, the number of rows is equal to the length of the result of func, the first p columns are the Jacobian, and the next p(p+1)/2 columns are the lower triangle of the second derivative (which is the Hessian for a scalar valued func). p is the length of x (dimension of the parameter space). f0 is the function value at the point where the matrix D was calculated. The genD arguments func, x, d, method, and method.args also are returned in the list.

References

Linfield, G.R. and Penny, J.E.T. (1989) "Microcomputers in Numerical Analysis." Halsted Press.

Bates, D.M. & Watts, D. (1980), "Relative Curvature Measures of Nonlinearity." J. Royal Statistics Soc. series B, 42:1-25

Bates, D.M. and Watts, D. (1988) "Non-linear Regression Analysis and Its Applications." Wiley.

Examples

    func <- function(x){c(x[1], x[1], x[2]^2)}
    z <- genD(func, c(2,2,5))
func <- function(x){c(x[1], x[1], x[2]^2)}
    z <- genD(func, c(2,2,5))

Numerical Gradient of a Function

Description

Calculate the gradient of a function by numerical approximation.

Usage

     grad(func, x, method="Richardson", side=NULL, method.args=list(), ...) 

    ## Default S3 method:
grad(func, x, method="Richardson", side=NULL,
      method.args=list(), ...)
 grad(func, x, method="Richardson", side=NULL, method.args=list(), ...) 

    ## Default S3 method:
grad(func, x, method="Richardson", side=NULL,
      method.args=list(), ...)

Arguments

`func`	a function with a scalar real result (see details).
`x`	a real scalar or vector argument to func, indicating the point(s) at which the gradient is to be calculated.
`method`	one of `"Richardson"`, `"simple"`, or `"complex"` indicating the method to use for the approximation.
`method.args`	arguments passed to method. Arguments not specified remain with their default values as specified in details
`side`	an indication of whether one-sided derivatives should be attempted (see details).
`...`	an additional arguments passed to `func`. WARNING: None of these should have names matching other arguments of this function.

Details

The function grad calculates a numerical approximation of the first derivative of func at the point x. Any additional arguments in ... are also passed to func, but the gradient is not calculated with respect to these additional arguments. It is assumed func is a scalar value function. If a vector x produces a scalar result then grad returns the numerical approximation of the gradient at the point x (which has the same length as x). If a vector x produces a vector result then the result must have the same length as x, and it is assumed that this corresponds to applying the function to each of its arguments (for example, sin(x)). In this case grad returns the gradient at each of the points in x (which also has the same length as x – so be careful). An alternative for vector valued functions is provided by jacobian.

If method is "simple", the calculation is done using a simple epsilon difference. For method "simple" method.args=list(eps=1e-4) is the default. Only eps is used by this method.

If method is "complex", the calculation is done using the complex step derivative approach of Lyness and Moler, described in Squire and Trapp. This method requires that the function be able to handle complex valued arguments and return the appropriate complex valued result, even though the user may only be interested in the real-valued derivatives. It also requires that the complex function be analytic. (This might be thought of as the complex equivalent of the requirement for continuity and smoothness of a real valued function.) So, while this method is extremely powerful it is applicable to a very restricted class of functions. Avoid this method if you do not know that your function is suitable. Your mistake may not be caught and the results will be spurious. For cases where it can be used, it is faster than Richardson's extrapolation, and it also provides gradients that are correct to machine precision (16 digits). For method "complex", method.args is ignored. The algorithm uses an eps of .Machine$double.eps which cannot (and should not) be modified.

If method is "Richardson", the calculation is done by Richardson's extrapolation (see e.g. Linfield and Penny, 1989, or Fornberg and Sloan, 1994.) This method should be used if accuracy, as opposed to speed, is important (but see method "complex" above). For this method method.args=list(eps=1e-4, d=0.0001, zero.tol=sqrt(.Machine$double.eps/7e-7), r=4, v=2, show.details=FALSE) is set as the default. d gives the fraction of x to use for the initial numerical approximation. The default means the initial approximation uses 0.0001 * x. eps is used instead of d for elements of x which are zero (absolute value less than zero.tol). zero.tol tolerance used for deciding which elements of x are zero. r gives the number of Richardson improvement iterations (repetitions with successly smaller d. The default 4 general provides good results, but this can be increased to 6 for improved accuracy at the cost of more evaluations. v gives the reduction factor. show.details is a logical indicating if detailed calculations should be shown.

The general approach in the Richardson method is to iterate for r iterations from initial values for interval value d, using reduced factor v. The the first order approximation to the derivative with respect to $x_{i}$ is

$f'_{i}(x) = <f(x_{1},\dots,x_{i}+d,\dots,x_{n}) - f(x_{1},\dots,x_{i}-d,\dots,x_{n})>/(2*d)$

This is repeated r times with successively smaller d and then Richardson extraplolation is applied.

If elements of x are near zero the multiplicative interval calculation using d does not work, and for these elements an additive calculation using eps is done instead. The argument zero.tol is used determine if an element should be considered too close to zero. In the iterations, interval is successively reduced to eventual be d/v^r and the square of this value is used in second derivative calculations (see genD) so the default zero.tol=sqrt(.Machine$double.eps/7e-7) is set to ensure the interval is bigger than .Machine$double.eps with the default d, r, and v.

If side is NULL then it is assumed that the point at which the calculation is being done is interior to the domain of the function. If the point is on the boundary of the domain then side can be used to indicate which side of the point x should be used for the calculation. If not NULL then it should be a vector of the same length as x and have values NA, +1, or -1. NA indicates that the usual calculation will be done, while +1, or -1 indicate adding or subtracting from the parameter point x. The argument side is not supported for all methods.

Since usual calculation with method "simple" uses only a small eps step to one side, the only effect of argument side is to determine the direction of the step. The usual calculation with method "Richardson" is symmetric, using steps to both sides. The effect of argument side is to take a double sized step to one side, and no step to the other side. This means that the center of the Richardson extrapolation steps is moving slightly in the reduction, and is not exactly on the boundary. (Warning: I am not aware of theory or published experimental evidence to support this, but the results in my limited testing seem good.)

Value

A real scalar or vector of the approximated gradient(s).

References

Linfield, G. R. and Penny, J. E. T. (1989) Microcomputers in Numerical Analysis. New York: Halsted Press.

Fornberg, B. and Sloan, D, M. (1994) “A review of pseudospectral methods for solving partial differential equations.” Acta Numerica, 3, 203-267.

Lyness, J. N. and Moler, C. B. (1967) “Numerical Differentiation of Analytic Functions.” SIAM Journal for Numerical Analysis, 4(2), 202-210.

Squire, William and Trapp, George (1998) “Using Complex Variables to Estimate Derivatives of Real Functions.” SIAM Rev, 40(1), 110-112.

Examples

  grad(sin, pi)
  grad(sin, (0:10)*2*pi/10)
  func0 <- function(x){ sum(sin(x))  }
  grad(func0 , (0:10)*2*pi/10)

  func1 <- function(x){ sin(10*x) - exp(-x) }

  curve(func1,from=0,to=5)

  x <- 2.04
  numd1 <- grad(func1, x)
  exact <- 10*cos(10*x) + exp(-x)
  c(numd1, exact, (numd1 - exact)/exact)

  x <- c(1:10)
  numd1 <- grad(func1, x)
  numd2 <- grad(func1, x, "complex")
  exact <- 10*cos(10*x) + exp(-x)
  cbind(numd1, numd2, exact, (numd1 - exact)/exact, (numd2 - exact)/exact)

  sc2.f <- function(x){
    n <- length(x)
    sum((1:n) * (exp(x) - x)) / n
    }

  sc2.g <- function(x){
    n <- length(x)
    (1:n) * (exp(x) - 1) / n
    }

  x0 <- rnorm(100)
  exact <- sc2.g(x0)

  g <- grad(func=sc2.f, x=x0)
  max(abs(exact - g)/(1 + abs(exact)))

  gc <- grad(func=sc2.f, x=x0, method="complex")
  max(abs(exact - gc)/(1 + abs(exact)))

  f <- function(x) if(x[1]<=0) sum(sin(x)) else  NA
  grad(f, x=c(0,0), method="Richardson", side=c(-1,  1))
grad(sin, pi)
  grad(sin, (0:10)*2*pi/10)
  func0 <- function(x){ sum(sin(x))  }
  grad(func0 , (0:10)*2*pi/10)

  func1 <- function(x){ sin(10*x) - exp(-x) }

  curve(func1,from=0,to=5)

  x <- 2.04
  numd1 <- grad(func1, x)
  exact <- 10*cos(10*x) + exp(-x)
  c(numd1, exact, (numd1 - exact)/exact)

  x <- c(1:10)
  numd1 <- grad(func1, x)
  numd2 <- grad(func1, x, "complex")
  exact <- 10*cos(10*x) + exp(-x)
  cbind(numd1, numd2, exact, (numd1 - exact)/exact, (numd2 - exact)/exact)

  sc2.f <- function(x){
    n <- length(x)
    sum((1:n) * (exp(x) - x)) / n
    }

  sc2.g <- function(x){
    n <- length(x)
    (1:n) * (exp(x) - 1) / n
    }

  x0 <- rnorm(100)
  exact <- sc2.g(x0)

  g <- grad(func=sc2.f, x=x0)
  max(abs(exact - g)/(1 + abs(exact)))

  gc <- grad(func=sc2.f, x=x0, method="complex")
  max(abs(exact - gc)/(1 + abs(exact)))

  f <- function(x) if(x[1]<=0) sum(sin(x)) else  NA
  grad(f, x=c(0,0), method="Richardson", side=c(-1,  1))

Calculate Hessian Matrix

Description

Calculate a numerical approximation to the Hessian matrix of a function at a parameter value.

Usage

    hessian(func, x, method="Richardson", method.args=list(), ...)

    ## Default S3 method:
hessian(func, x, method="Richardson",
        method.args=list(), ...)
hessian(func, x, method="Richardson", method.args=list(), ...)

    ## Default S3 method:
hessian(func, x, method="Richardson",
        method.args=list(), ...)

Arguments

`func`	a function for which the first (vector) argument is used as a parameter vector.
`x`	the parameter vector first argument to func.
`method`	one of `"Richardson"` or `"complex"` indicating the method to use for the approximation.
`method.args`	arguments passed to method. See `grad`. (Arguments not specified remain with their default values.)
`...`	an additional arguments passed to `func`. WARNING: None of these should have names matching other arguments of this function.

Details

The function hessian calculates an numerical approximation to the n x n second derivative of a scalar real valued function with n-vector argument.

The argument method can be "Richardson" or "complex". Method "simple" is not supported.

For method "complex" the Hessian matrix is calculated as the Jacobian of the gradient. The function grad with method "complex" is used, and method.args is ignored for this (an eps of .Machine$double.eps is used). However, jacobian is used in the second step, with method "Richardson" and argument method.args is used for this. The default is method.args=list(eps=1e-4, d=0.1, zero.tol=sqrt(.Machine$double.eps/7e-7), r=4, v=2, show.details=FALSE). (These are the defaults for hessian with method "Richardson", which are slightly different from the defaults for jacobian with method "Richardson".) See addition comments in grad before choosing method "complex".

Methods "Richardson" uses genD and extracts the second derivative. For this method method.args=list(eps=1e-4, d=0.1, zero.tol=sqrt(.Machine$double.eps/7e-7), r=4, v=2, show.details=FALSE) is set as the default. hessian does one evaluation of func in order to do some error checking before calling genD, so the number of function evaluations will be one more than indicated for genD.

The argument side is not supported for second derivatives and since ... are passed to func there may be no error message if it is specified.

Value

An n by n matrix of the Hessian of the function calculated at the point x.

Examples

  sc2.f <- function(x){
    n <- length(x)
    sum((1:n) * (exp(x) - x)) / n
    }

  sc2.g <- function(x){
    n <- length(x)
    (1:n) * (exp(x) - 1) / n
    }

  x0 <- rnorm(5)
  hess <- hessian(func=sc2.f, x=x0)
  hessc <- hessian(func=sc2.f, x=x0, "complex")
  all.equal(hess, hessc, tolerance = .Machine$double.eps)
  
#  Hessian = Jacobian of the gradient
  jac  <- jacobian(func=sc2.g, x=x0)
  jacc <- jacobian(func=sc2.g, x=x0, "complex")
  all.equal(hess, jac, tolerance = .Machine$double.eps)
  all.equal(hessc, jacc, tolerance = .Machine$double.eps)
sc2.f <- function(x){
    n <- length(x)
    sum((1:n) * (exp(x) - x)) / n
    }

  sc2.g <- function(x){
    n <- length(x)
    (1:n) * (exp(x) - 1) / n
    }

  x0 <- rnorm(5)
  hess <- hessian(func=sc2.f, x=x0)
  hessc <- hessian(func=sc2.f, x=x0, "complex")
  all.equal(hess, hessc, tolerance = .Machine$double.eps)
  
#  Hessian = Jacobian of the gradient
  jac  <- jacobian(func=sc2.g, x=x0)
  jacc <- jacobian(func=sc2.g, x=x0, "complex")
  all.equal(hess, jac, tolerance = .Machine$double.eps)
  all.equal(hessc, jacc, tolerance = .Machine$double.eps)

Gradient of a Vector Valued Function

Description

Calculate the m by n numerical approximation of the gradient of a real m-vector valued function with n-vector argument.

Usage

    jacobian(func, x, method="Richardson", side=NULL, method.args=list(), ...) 

    ## Default S3 method:
jacobian(func, x, method="Richardson", side=NULL,
       method.args=list(), ...)
jacobian(func, x, method="Richardson", side=NULL, method.args=list(), ...) 

    ## Default S3 method:
jacobian(func, x, method="Richardson", side=NULL,
       method.args=list(), ...)

Arguments

`func`	a function with a real (vector) result.
`x`	a real or real vector argument to func, indicating the point at which the gradient is to be calculated.
`method`	one of `"Richardson"`, `"simple"`, or `"complex"` indicating the method to use for the approximation.
`method.args`	arguments passed to method. See `grad`. (Arguments not specified remain with their default values.)
`...`	any additional arguments passed to `func`. WARNING: None of these should have names matching other arguments of this function.
`side`	an indication of whether one-sided derivatives should be attempted (see details in function `grad`).

Details

For $f:R^n -> R^m$ calculate the $m x n$ Jacobian $dy/dx$ . The function jacobian calculates a numerical approximation of the first derivative of func at the point x. Any additional arguments in ... are also passed to func, but the gradient is not calculated with respect to these additional arguments.

If method is "Richardson", the calculation is done by Richardson's extrapolation. See link{grad} for more details. For this method method.args=list(eps=1e-4, d=0.0001, zero.tol=sqrt(.Machine$double.eps/7e-7), r=4, v=2, show.details=FALSE) is set as the default.

If method is "simple", the calculation is done using a simple epsilon difference. For method "simple" method.args=list(eps=1e-4) is the default. Only eps is used by this method.

If method is "complex", the calculation is done using the complex step derivative approach. See addition comments in grad before choosing this method. For method "complex", method.args is ignored. The algorithm uses an eps of .Machine$double.eps which cannot (and should not) be modified.

Value

A real m by n matrix.

Examples

   func2 <- function(x) c(sin(x), cos(x))
   x <- (0:1)*2*pi
   jacobian(func2, x)
   jacobian(func2, x, "complex")
func2 <- function(x) c(sin(x), cos(x))
   x <- (0:1)*2*pi
   jacobian(func2, x)
   jacobian(func2, x, "complex")

Package 'numDeriv'

Help Index

Accurate Numerical Derivatives

Description

Details

Author(s)

References

Accurate Numerical Derivatives

Description

Details

Generate Bates and Watts D Matrix

Description

Usage

Arguments

Details

Value

References

See Also

Examples

Numerical Gradient of a Function

Description

Usage

Arguments

Details

Value

References

See Also

Examples

Calculate Hessian Matrix

Description

Usage

Arguments

Details

Value

See Also

Examples

Gradient of a Vector Valued Function

Description

Usage

Arguments

Details

Value

See Also

Examples