Package 'minimaxApprox' reference manual

Title:	Implementation of Remez Algorithm for Polynomial and Rational Function Approximation
Description:	Implements the algorithm of Remez (1962) for polynomial minimax approximation and of Cody et al. (1968) <doi:10.1007/BF02162506> for rational minimax approximation.
Authors:	Avraham Adler [aut, cre, cph]
Maintainer:	Avraham Adler <[email protected]>
License:	MPL-2.0
Version:	0.4.3
Built:	2024-11-19 06:32:28 UTC
Source:	CRAN

Implementation of Remez Algorithm for Polynomial and Rational Function Approximation

Description

Implements the algorithm of Remez (1962) for polynomial minimax approximation and of Cody et al. (1968) <doi:10.1007/BF02162506> for rational minimax approximation.

Details

The DESCRIPTION file:

Package:	minimaxApprox
Type:	Package
Title:	Implementation of Remez Algorithm for Polynomial and Rational Function Approximation
Version:	0.4.3
Date:	2024-06-20
Authors@R:	person(given="Avraham", family="Adler",role=c("aut", "cre", "cph"), email="[email protected]", comment = c(ORCID = "0000-0002-3039-0703"))
Description:	Implements the algorithm of Remez (1962) for polynomial minimax approximation and of Cody et al. (1968) <doi:10.1007/BF02162506> for rational minimax approximation.
License:	MPL-2.0
URL:	https://github.com/aadler/MiniMaxApprox
BugReports:	https://github.com/aadler/MiniMaxApprox/issues
Imports:	stats, graphics
Suggests:	tinytest, covr
ByteCompile:	yes
NeedsCompilation:	yes
Encoding:	UTF-8
UseLTO:	yes
Packaged:	2024-06-20 06:57:53 UTC; Parents
Author:	Avraham Adler [aut, cre, cph] (<https://orcid.org/0000-0002-3039-0703>)
Maintainer:	Avraham Adler <[email protected]>
Repository:	CRAN
Date/Publication:	2024-06-20 07:10:02 UTC

Index of help topics:

coef.minimaxApprox      Extract coefficients from a '"minimaxApprox"'
                        object
minimaxApprox           Minimax Approximation of Functions
minimaxApprox-package   Implementation of Remez Algorithm for
                        Polynomial and Rational Function Approximation
minimaxErr              Evaluate the Minimax Approximation Error
minimaxEval             Evaluate Minimax Approximation
plot.minimaxApprox      Plot errors from a '"minimaxApprox"' object
print.minimaxApprox     Print method for a '"minimaxApprox object"'

Author(s)

Avraham Adler [aut, cre, cph] (<https://orcid.org/0000-0002-3039-0703>)

Maintainer: Avraham Adler <[email protected]>

Extract coefficients from a `"minimaxApprox"` object

Description

Extracts the numerator and denominator vectors from a "minimaxApprox" object. For objects with both Chebyshev and monomial coefficients, it will extract both.

Usage

## S3 method for class 'minimaxApprox'
coef(object, ...)
## S3 method for class 'minimaxApprox'
coef(object, ...)

Arguments

`object`	An object inheriting from class `"minimaxApprox"`.
`...`	Other arguments.

Value

Coefficients extracted from the "minimaxApprox" object. A list containing:

`a`	The polynomial coefficients or the rational numerator coefficients.
`b`	The rational denominator coefficients. Missing for polynomial approximation.
`aMono`	The polynomial coefficients or the rational numerator coefficients for the monomial basis when the approximation was done using Chebyshev polynomials. Missing if only the monomial basis was used.
`bMono`	The rational denominator coefficients for the monomial basis when the approximation was done using Chebyshev polynomials. Missing if either only the monomial basis was used or for polynomial approximation.

Author(s)

Avraham Adler [email protected]

Examples

PP <- minimaxApprox(exp, 0, 1, 5)
coef(PP)
identical(unlist(coef(PP), use.names = FALSE), c(PP$a, PP$aMono))

RR <- minimaxApprox(exp, 0, 1, c(2, 3), basis = "m")
coef(RR)
identical(coef(RR), list(a = RR$a, b = RR$b))
PP <- minimaxApprox(exp, 0, 1, 5)
coef(PP)
identical(unlist(coef(PP), use.names = FALSE), c(PP$a, PP$aMono))

RR <- minimaxApprox(exp, 0, 1, c(2, 3), basis = "m")
coef(RR)
identical(coef(RR), list(a = RR$a, b = RR$b))

Minimax Approximation of Functions

Description

Calculates minimax approximations to functions. Polynomial approximation uses the Remez (1962) algorithm. Rational approximation uses the Cody-Fraser-Hart (Cody et al., 1968) version of the algorithm. When using monomials as the polynomial basis, the Compensated Horner Scheme of Langlois et al. (2006) is used.

Usage

minimaxApprox(fn, lower, upper, degree, relErr = FALSE, basis ="Chebyshev",
              xi = NULL, opts = list())
minimaxApprox(fn, lower, upper, degree, relErr = FALSE, basis ="Chebyshev",
              xi = NULL, opts = list())

Arguments

`fn`	function; A vectorized univariate function having `x` as its first argument. This could be a built-in R function, a predefined function, or an anonymous function defined in the call; see Examples.
`lower`	numeric; The lower bound of the approximation interval.
`upper`	numeric; The upper bound of the approximation interval.
`degree`	integer; Either a single value representing the requested degree for polynomial approximation or a vector of length 2 representing the requested degrees of the numerator and denominator for rational approximation.
`relErr`	logical; If `TRUE`, calculate the minimax approximation using relative error. The default is `FALSE` which uses absolute error.
`basis`	character; Which polynomial basis to use in the analysis. `"Monomial"` uses the standard $x^k$ basis. `"Chebyshev"` uses the Chebyshev polynomials of the first kind, $T_k$ . The default is `"Chebyshev"`, and the parameter is case-insensitive and may be abbreviated.
`xi`	numeric; For rational approximation, a vector of initial points of the correct length— $\sum(\code{degree}) + 2$ . If missing, the approximation will use the appropriate Chebyshev nodes. Polynomial approximation always uses Chebyshev nodes and will ignore `xi` with a message.
`opts`	list; Configuration options including: `maxiter`: integer; The maximum number of iterations to attempt convergence. Defaults to 100. `miniter`: integer; The minimum number of iterations before allowing convergence. Defaults to 10. `conviter`: integer; The number of successive iterations with the same results allowed before assuming no further convergence is possible. Defaults to 30. Will overwrite `maxiter` and `miniter` if `conviter` is explicitly passed and is larger than either one. `showProgress`: logical; If `TRUE` will print error values at each iteration. `convrat`: numeric; The convergence ratio tolerance. Defaults to $1 + 1 \times 10^{-9}$ . See Details. `tol`: numeric; The absolute difference tolerance. Defaults to $1 \times 10^{-14}$ . See Details. `tailtol`: numeric; The tolerance of the coefficient of the largest power of `x` to be ignored when performing the polynomial approximation a second time. Defaults to the smaller of $1 \times 10^{-10}$ or $\frac{\code{upper} - \code{lower}}{10^6}$ . Set to `NULL` to skip the `degree + 1` check completely. See Details. `ztol`: numeric; The tolerance for each polynomial or rational numerator or denominator coefficient's contribution to not to be set to 0. Similar to polynomial `tailtol` but applied at each step of the algorithm. Defaults to `NULL` which leaves all coefficients as they are regardless of magnitude. See Details.

Details

Convergence

The function implements the Remez algorithm using linear approximation, chiefly as described by Cody et al. (1968). Convergence is considered achieved when all three of the following criteria are met:

The observed error magnitudes are within tolerance of the expected error—the Distance Test.
The observed error magnitudes are within tolerance of each other—the Magnitude Test.
The observed error signs oscillate—the Oscillation Test.

“Within tolerance” can be met in one of two ways:

Difference: The difference between the absolute magnitudes is less than or equal to tol.
Ratio: The ratio between the absolute magnitudes of the larger and smaller is less than or equal to convrat.

For efficiency, the Distance Test is taken between the absolute value of the largest observed error and the absolute value of the expected error. Similarly, the Magnitude Test is taken between the absolute value of the largest observed error and the absolute value of the smallest observed error. Both tests can be passed by either being within tol or convrat as described above. However, when the Difference test returns values less than machine precision, it is ignored in favor of the Ratio test.

When the error values remain within tolerance of each other over conviter iterations, the algorithm will stop, as it is expected that no further precision will be gained by continued iterations.

Polynomial Evaluation

Monomial polynomials are evaluated using the Compensated Horner Scheme of Langlois et al. (2006) to enhance both stability and precision. Chebyshev polynomials are evaluated normally. There may be cases where the algorithm will fail using the monomial basis but succeed using Chebyshev polynomials and vice versa. The default is to use the Chebyshev polynomials.

Polynomial Algorithm “Singular Error” Response

When too high of a degree is requested for the tolerance of the algorithm, it often fails with a singular matrix error. In this case, for the polynomial version, the algorithm will try looking for an approximation of degree n + 1. If it finds one, and the contribution of that coefficient to the approximation is $\le$ tailtol, it will ignore that coefficient and return the resulting degree n polynomial, as the largest coefficient is effectively 0. The contribution is measured by multiplying that coefficient by the endpoint with the larger absolute magnitude raised to the n + 1 power. This is done to prevent errors in cases where a very small coefficient is found on a range with very large absolute values and the resulting contribution to the approximation is not de minimis. Setting tailtol to NULL will skip the n + 1 test completely.

Close-to-Zero Tolerance

For each step of the algorithms' iterations, the contribution of the found coefficient to the total sum (as measured in the above section) is compared to the ztol option. When less than or equal to ztol, that coefficient is set to 0. Setting ztol to NULL skips the test completely. For intervals near or containing zero, setting this option to anything other than NULL may result in either non-convergence or poor results. It is recommended to keep it as NULL, although there are edge cases where it may allow convergence where a standard call may fail.

Value

minimaxApprox returns an object of class "minimaxApprox" which inherits from the class list.

The generic accessor function coef will extract the numerator and denominator vectors. There are also default print and plot methods.

An object of class "minimaxApprox" is a list containing the following components:

`a`	The polynomial or rational numerator coefficients. When using Chebyshev polynomials, these are the coefficients for $T_k$ . When using monomials, these are the coefficients for $x^k$ .
`b`	The rational denominator coefficients. When using Chebyshev polynomials, these are the coefficients for $T_k$ . When using monomials, these are the coefficients for $x^k$ . Missing for polynomial approximation.
`aMono`	When using Chebyshev polynomials, these are the polynomial or rational numerator coefficients for monomial expansion in $x^k$ . Missing for monomial-based approximation.
`bMono`	When using Chebyshev polynomials, these are the rational denominator coefficients for monomial expansion in $x^k$ . Missing for both polynomial and monomial-based rational approximation.
`ExpErr`	The absolute value of the expected error as calculated by the Remez algorithms.
`ObsErr`	The absolute value of largest observed error between the function and the approximation at the extremal points.
`iterations`	The number of iterations of the algorithm. This does not include any iterations required to converge the error value in rational approximation.
`Extrema`	The extrema at which the minimax error was achieved.
`Warning`	A logical flag indicating if any warnings were thrown.

The object also contains the following attributes:

`type`	"Rational" or "Polynomial".
`basis`	"Monomial" or "Chebyshev".
`func`	The function being approximated.
`range`	The range on which the function is being approximated.
`relErr`	A logical indicating that relative error was used. If `FALSE`, then absolute error was used.
`tol`	The tolerance used for the Distance Test.
`convrat`	The tolerance used for the Magnitude Test.

Note

At present, the algorithms are implemented using machine double precision, which means that the approximations are at best slightly worse. Research proceeds on more precise, stable, and efficient implementations. So long as the package remains in an experimental state—noted by a 0 major version—the API may change at any time.

Author(s)

Avraham Adler [email protected]

References

Remez, E. I. (1962) General computational methods of Chebyshev approximation: The problems with linear real parameters. US Atomic Energy Commission, Division of Technical Information. AEC-tr-4491

Fraser W. and Hart J. F. (1962) “On the computation of rational approximations to continuous functions”, Communications of the ACM, 5(7), 401–403, doi:10.1145/368273.368578

Cody, W. J. and Fraser W. and Hart J. F. (1968) “Rational Chebyshev approximation using linear equations”, Numerische Mathematik, 12, 242–251, doi:10.1007/BF02162506

Langlois, P. and Graillat, S. and Louvet, N. (2006) “Compensated Horner Scheme”, in Algebraic and Numerical Algorithms and Computer-assisted Proofs. Dagstuhl Seminar Proceedings, 5391, doi:10.4230/DagSemProc.05391.3

Examples

minimaxApprox(exp, 0, 1, 5)                              # Built-in & polynomial

fn <- function(x) sin(x) ^ 2 + cosh(x)                   # Pre-defined
minimaxApprox(fn, 0, 1, c(2, 3), basis = "m")            # Rational

minimaxApprox(function(x) x ^ 3 / sin(x), 0.7, 1.6, 6L)  # Anonymous

fn <- function(x) besselJ(x, nu = 0)                     # More than one input
b0 <- 0.893576966279167522                               # Zero of besselY
minimaxApprox(fn, 0, b0, c(3L, 3L))                      # Cf. DLMF 3.11.19
minimaxApprox(exp, 0, 1, 5)                              # Built-in & polynomial

fn <- function(x) sin(x) ^ 2 + cosh(x)                   # Pre-defined
minimaxApprox(fn, 0, 1, c(2, 3), basis = "m")            # Rational

minimaxApprox(function(x) x ^ 3 / sin(x), 0.7, 1.6, 6L)  # Anonymous

fn <- function(x) besselJ(x, nu = 0)                     # More than one input
b0 <- 0.893576966279167522                               # Zero of besselY
minimaxApprox(fn, 0, b0, c(3L, 3L))                      # Cf. DLMF 3.11.19

Evaluate the Minimax Approximation Error

Description

Evaluates the difference between the function and the minimax approximation at x.

Usage

minimaxErr(x, mmA)
minimaxErr(x, mmA)

Arguments

`x`	a numeric vector
`mmA`	a `"minimaxApprox"` return object

Details

This is a convenience function to evaluate the approximation error at x. It will use the same polynomial basis as was used in the approximation; see minimaxApprox for more details.

Value

A vector of the same length as x containing the approximation error values.

Author(s)

Avraham Adler [email protected]

Examples

# Show results
x <- seq(0, 0.5, length.out = 11L)
mmA <- minimaxApprox(exp, 0, 0.5, 5L)
err <- minimaxEval(x, mmA) - exp(x)
all.equal(err,  minimaxErr(x, mmA))

# Plot results
x <- seq(0, 0.5, length.out = 1001L)
plot(x, minimaxErr(x, mmA), type = "l")
# Show results
x <- seq(0, 0.5, length.out = 11L)
mmA <- minimaxApprox(exp, 0, 0.5, 5L)
err <- minimaxEval(x, mmA) - exp(x)
all.equal(err,  minimaxErr(x, mmA))

# Plot results
x <- seq(0, 0.5, length.out = 1001L)
plot(x, minimaxErr(x, mmA), type = "l")

Evaluate Minimax Approximation

Description

Evaluates the rational or polynomial approximation stored in mmA at x.

Usage

minimaxEval(x, mmA, basis = "Chebyshev")
minimaxEval(x, mmA, basis = "Chebyshev")

Arguments

`x`	a numeric vector
`mmA`	a `"minimaxApprox"` return object
`basis`	character; Which polynomial basis to use in to evaluate the function; see `minimaxApprox` for more details. If Chebyshev is requested but the analysis used only monomials, the calculation will proceed using the monomials with a message. The default is `"Chebyshev"`, and the parameter is case-insensitive and may be abbreviated.

Details

This is a convenience function to evaluate the approximation at x.

Value

A vector of the same length as x containing the approximated values.

Author(s)

Avraham Adler [email protected]

Examples

# Show results
x <- seq(0, 0.5, length.out = 11L)
mmA <- minimaxApprox(exp, 0, 0.5, 5L)
apErr <- abs(exp(x) - minimaxEval(x, mmA))
all.equal(max(apErr), mmA$ExpErr)

# Plot results
curve(exp, 0.0, 0.5, lwd = 2)
curve(minimaxEval(x, mmA), 0.0, 0.5, add = TRUE, col = "red", lty = 2L, lwd = 2)
# Show results
x <- seq(0, 0.5, length.out = 11L)
mmA <- minimaxApprox(exp, 0, 0.5, 5L)
apErr <- abs(exp(x) - minimaxEval(x, mmA))
all.equal(max(apErr), mmA$ExpErr)

# Plot results
curve(exp, 0.0, 0.5, lwd = 2)
curve(minimaxEval(x, mmA), 0.0, 0.5, add = TRUE, col = "red", lty = 2L, lwd = 2)

Plot errors from a `"minimaxApprox"` object

Description

Produces a plot of the error of the "minimaxApprox" object, highlighting the error extrema and bounds.

Usage

## S3 method for class 'minimaxApprox'
plot(x, y, ...)## S3 method for class 'minimaxApprox'
plot(x, y, ...)

Arguments

`x`	An object inheriting from class `"minimaxApprox"`.
`y`	Ignored. In call as required by R in Writing R Extensions:chapter 7.
`...`	Further arguments to plot. Specifically to pass `ylim` to allow for zooming in or out.

Value

No return value; called for side effects.

Author(s)

Avraham Adler [email protected]

Examples

PP <- minimaxApprox(exp, 0, 1, 5)
plot(PP)
PP <- minimaxApprox(exp, 0, 1, 5)
plot(PP)

Print method for a `"minimaxApprox object"`

Description

Provides a more human-readable output of a "minimaxApprox" object.

Usage

## S3 method for class 'minimaxApprox'
print(x, digits = 14L, ...)
## S3 method for class 'minimaxApprox'
print(x, digits = 14L, ...)

Arguments

`x`	An object inheriting from class `"minimaxApprox"`.
`digits`	integer; Number of digits to which to round the ratio.
`...`	Further arguments to `print`.

Details

To print the raw "minimaxApprox" object use print.default.

Value

No return value; called for side effects.

Author(s)

Avraham Adler [email protected]

Examples

PP <- minimaxApprox(sin, 0, 1, 8)
PP
print(PP, digits = 2L)
print.default(PP)
PP <- minimaxApprox(sin, 0, 1, 8)
PP
print(PP, digits = 2L)
print.default(PP)

Package 'minimaxApprox'

Help Index

Implementation of Remez Algorithm for Polynomial and Rational Function Approximation

Description

Details

Author(s)

Extract coefficients from a "minimaxApprox" object

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

Minimax Approximation of Functions

Description

Usage

Arguments

Details

Convergence

Polynomial Evaluation

Polynomial Algorithm “Singular Error” Response

Close-to-Zero Tolerance

Value

Note

Author(s)

References

See Also

Examples

Evaluate the Minimax Approximation Error

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Evaluate Minimax Approximation

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Plot errors from a "minimaxApprox" object

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

Print method for a "minimaxApprox object"

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Extract coefficients from a `"minimaxApprox"` object

Plot errors from a `"minimaxApprox"` object

Print method for a `"minimaxApprox object"`