### 17.2.2 Rational minimax approximation

The minimax command finds the rational function which approximates a continuous function f:[a,b]→ℝ most closely in the sense of ℓ∞ norm. It operates exclusively in floating-point arithmetic.

- minimax takes three mandatory argument and a sequence of optional arguments:
  - expr, a univariate expression representing the function f.
  - x=a..b, a variable with bounds.
  - deg, which is either a positive integer n or a list [n,m] where n is a negative and m a positive integer, specifying the degrees of the numerator (and denominator) of the approximant (by default, m=0).
  - Optionally, opts, a sequence of options each of which is one of:
    - limit=maxiter, where maxiter is a positive integer specifying the number of iterations of the Remez algorithm (by default, maxiter=100).
    - rand⟨=N⟩, where N is a positive integer specifying the number of tries with initial node skewing (by default, N=1 and no skewing is performed).
    - threshold=tol, where tol is a real number greater than 1 specifying the terminating criterion maxx∈[a,b] e(x)<tol·minx∈[a,b] e(x) for the Remez algorithm, where e(x)=|f(x)−r(x)| is the error function in the current iteration (by default, tol=1.02).
- minimax(expr,x=a..b,deg⟨,limit=maxiter⟩) uses the Remez method (see e.g. here) to find the rational function r(x)=p(x)/q(x), where p is a polynomial of degree n and q a polynomial of degree m normalized such that its trailing coefficient is equal to 1, which minimizes the ℓ∞ error when approximating expr on [a,b]. If r has no singularities in [a,b], then a list containing ||f−r||∞ and r(x) is returned. Otherwise, an approximation of 1/b−a∫ab|f(x)−r(x)|d x, computed by using Simpson’s rule with 20(n+m+2) steps, is returned as the first element (this may serve as a rough estimation of fitness outside poles) and a warning is printed in the message area.
- By specifying only the parameter n, you get an approximation of f by a polynomial. This is faster and the result is always well-defined, but rational approximants provide more flexibility and can be significantly more accurate even for smaller values of n and m (these should be chosen so that r has no singularities in [a,b], however).
- With the rand⟨=N⟩ option, minimax skews the initial (Chebyshev) nodes randomly before proceeding to the first Remez step. This may help the algorithm to converge to a well-defined result when m>0. If N is specified, then the entire computation is performed N times and the best result is selected. This will be a pole-free function when possible. The rand option is usually not needed when m=0, i.e. when the minimax polynomial is sought.

#### Example

Find a function of form r(x)=a2x2+a1x+a0/b2x2+b1x+b0 which is an optimal ℓ∞-approximation of f(x)=ln(x+1)/2+sinx on the segment [0,7]. Define f by entering:

f:=ln(x+1)/(2+sin(x)):;

Now compute the approximation by entering:

fit:=minimax(f,x=0..7,[2,2])

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0.0759657940359,

−0.00264534091503 x2+0.0393947055011 x+0.0759192001346
0.0386492470797 x2−0.368513748981 x+1

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To display the approximation, enter:

tol,r:=op(fit):; plot(ln(x+1)/(2+sin(x)),x=0..7); plot(r,x=0..7,color=blue)

(Here, r(x) is drawn in blue.) You can conclude that max0≤ x≤ 7|f(x)−r(x)|=tol. Indeed:

maximize(abs(f-r),x=0..7)

0.0759657940359 |  |  |  |  |  |  |  |  |  |

The maximal absolute error is attained at x=3.45532968966.

As another example, consider the function f(x)=x esinx/10+7cosx for x∈[0,10]. To show the graph, enter:

f:=x*exp(sin(x))/(10+7*cos(x)); D:=0..10:; plot(f,x=D)

In the examples that follow, approximation functions are not output to save space.

To obtain the ℓ∞ error of the 15th order polynomial approximation, enter:

minimax(f,x=D,15)[0]

0.0800812074329 |  |  |  |  |  |  |  |  |  |

By requesting a rational approximant, you can achieve significantly better accuracy with polynomials of lower degrees:

p:=minimax(f,x=D,[8,8])[0]

0.000495628620328 |  |  |  |  |  |  |  |  |  |

Sometimes you get poles in the approximant, for example:

minimax(f,x=D,[10,3])[0]

Warning: the result is undefined at point(s) 3.03555290496

0.206238829817 |  |  |  |  |  |  |  |  |  |

By using the rand option, you obtain a pole-free result:

p:=minimax(f,x=D,[10,3],rand=30)[0]

Evaluation time: 6.11

0.0364254964552 |  |  |  |  |  |  |  |  |  |