- Solving Numerical Equations (numerical)
- Numerical Iteration (numerical)
- Newton Methods (numerical)
- Numerical Differentiation (numerical)
- FFT, Folding, Filters (numerical)
- Interpolation (numerical)
- Hermite Interpolation (numerical)
- Remez Algorithm (numerical)
- Nonlinear Optimization (numerical)
- Bezier Curves (numerical)
- Splines (numerical)
- Adaptive Evaluation (numerical)
- Numerical Integration (numerical)
- Gauss-Quadrature (numerical)
- Differential Equations (numerical)
- Sparse Matrices (numerical)
- Gauss-Seidel for Sparse Systems (numerical)
- Incidence Matrices (numerical)
- Exact Computation (numerical)
- Interval Solvers and Guaranteed Solutions (numerical)

Various numerical methods.

For demos of some of these algorithms refer to the following notebook.

functionbisect(f$:call, a:number, b:number, y:number=0, eps=epsilon())

Bisection method to solve f(x)=y in [a,b] f is either a function of one real variable, or an expression in x. Additional parameters to "bisect" after a semicolon will be passed to the function f. The bisection routine assumes a zero in the interval, i.e., it assumes different signs of f(a) and f(b). The routine bisects the interval until the desired accuracy is reached. It is a very stable function. It is possible to specify the accuracy epsilon with eps=... as an additional parameter. The default is the internal epsilon. The absolute error is used. There is the interval alternative ibisect(). f : function or expression in x a,b : interval bounds y : target value eps : epsilon for the absolute error. >bisect("x^2",0,2,y=2) // solve x^2=2 1.41421356237 >bisect("x^2",0,2,y=2,eps=0.001) 1.41455078125 >function f(x,a) := exp(-a*x)*cos(x) >x=bisect("f",0,pi/2;2,y=0.5) // solve f(x,2)=0.5 0.320449786201 >f(x,2) // test 0.5 See:

newton (Numerical Algorithms),

newton (Maxima Documentation),

secant (Numerical Algorithms),

ibisect (Numerical Algorithms),

call (Euler Core)

functionintbisect(f$:call, a:number, b:number, y:number=0)

Bisection method to find an integer solution for f(x)=y. This function should only be used for monotone functions. It makes sense for functions, which are defined only for integer values. The solution satisfies f(floor(x))>=y, f(ceil(x))<=y (reversed if f is increasing). In the last step, x interpolates f in a linear way between floor(x) and ceil(x). y : target value >intbisect("chidis(6.5,x)",1,10,y=0.5) 7.15896300259 >chidis(6.5,[7,8]) [0.517277, 0.408592] See:

call (Euler Core)

functionsecant(f$:call, a:complex scalar, b=none, y:number=0, eps=none)

Secant method to solve f(x)=y in [a,b] f is either a function of one real variable, or an expression or a call collection in x. Additional parameters to "secant" after a semicolon will be passed to the function f. The secant routine uses an approximation of the derivative to find a better approximation to the solution. This function is almost as good as the Newton method, but does not require the derivative of f. Note that the result is not guaranteed to be within the interval [a,b]. For an alternative, see secantin(). Moreover, the function may fail, if the approximated derivative is too close to 0. The parameter b is optional. In this case, the function starts with an interval around a. The method can also be used for complex functions. It is possible to specify the accuracy with eps=... as last parameter. f : function or expression in x a,b : interval bounds, b is optional y : target value >bisect("x^2",0,2,y=2) 1.41421356237 >secant("x^2",1,y=2) // expression 1.41421356237 >secant({{"x^2-a",a=2}},1) // call collection 1.41421356237 >function f(x,a) := x^2-a >secant("f",5;3), %^2 1.73205080757 3 >secant("x^2",1,y=I) // complex equation x^2=I 0.707107+0.707107i See:

bisect (Numerical Algorithms),

newton (Numerical Algorithms),

newton (Maxima Documentation),

secantin (Numerical Algorithms),

call (Euler Core)

functionsecantin(f$:call, a:real scalar, b:real scalar, y:number=0,eps=epsilon())

Secant method to solve f(x)=y in [a,b] Works like the secant method. But the result is guaranteed to be in [a,b]. Use this for functions, which are not defined outside of [a.b]. The parameter "eps" is used for the absolute error. >secant("sqrt(x)*exp(-x)",0.4,y=0.3) // first leads to x<0 Floating point error! Error in sqrt Error in expression: sqrt(x)*exp(-x) secant: x0=x1; y0=y1; x1=x2; y1=f$(x2,args())-y; >secantin("sqrt(x)*exp(-x)",0,0.4,y=0.3) // solve in [0,0.4] 0.112769901579 See:

bisect (Numerical Algorithms),

newton (Numerical Algorithms),

newton (Maxima Documentation),

call (Euler Core)

functionsolve(f$:call, a:complex scalar, b=none, y:number=0, eps=none)

Solve f(x)=y near a. Alias to "secant". See:

secant (Numerical Algorithms),

call (Euler Core)

functionroot(f$:string, %x, eps=none)

Find the root of an expression f by changing the variable x. This is a function for interactive use. It will not work properly inside of functions. Use solve() instead. f must be an expression in several variables. All variables but x are constant, and only x is changed to solve f(x,...)=0. All variables in the expression must be global variables. Note that the actual name of the variable x may be different from "x", so you can solve for any variable in the expression f. But the parameter must be a variable. f : expression in several variables %x : variable to be solved for >a=2; x=2; root("x^x-a",x); "a = "+a, "x = "+x, a = 2 x = 1.55961046946 >a=2; x=2; root("x^x-a",a); "a = "+a, "x = "+x, a = 4 x = 2 See:

secant (Numerical Algorithms),

solve (Numerical Algorithms),

solve (Maxima Documentation),

newton (Numerical Algorithms),

newton (Maxima Documentation)

functioniterate(f$:call, x:numerical, n:natural=0, till=none, eps=none, best=false)

Iterates f starting from x to convergence. This function iterates xnew=f(x), until xnew~=x (using the internal epsilon or the optional eps), or the maximal number of iterations n is exceeded. If >best is set the iteration will continue until abs(x-f(x)) does no longer get smaller. It returns the limit for n=0 (the default), or n iterated values. There is a "till" expression, which can also stop the iteration. f is either a function of a scalar returning a scalar, or a function of a vector returning a vector. The function will work with column or row vectors, and even for matrices, if n==0. f can also be an expression or a call collection of a variable x. The iteration can also be used for intervals or interval vectors. In this case, The iteration will stop until the left and right interval bounds are closer than epsilon. If >best is set the iteration continues until the interval length does not get any smaller. f : function or expression x : start point for the algorithm n : optional number of steps (0 = till convergence) till : optional end condition best : Iterate until best result is achieved (takes long if the limit is 0). >iterate("cos",1) // solves cos(x)=x, iterating from 1 0.739085133216 >longest iterate("(x+2/x)/2",1,n=7)' 1 1.5 1.416666666666667 1.41421568627451 1.41421356237469 1.414213562373095 1.414213562373095 1.414213562373095 >iterate("x+1",1000,till="isprime(x)") 1009 >function f(x,A,b) := A.x-b >A=random(3,3)/2; b=sum(A); >iterate("f",zeros(3,1);A,b) -11.7766 -12.4523 -14.938 >iterate("(middle(x)+2/x)/2",~1,2~) // interval iteration ~1.414213562372,1.414213562374~ >iterate("(x+I/x)/2",1) // complex iteration 0.707107+0.707107i Returns {x,n}, where n is the number of iterations needed See:

niterate (Numerical Algorithms),

call (Euler Core)

functionniterate(f$:call, x:numerical, n:natural, till=none)

Iterates f n times starting from x. Works like iterate with n>0. f : function or expression x : start point for the algorithm n : number of iterations till : optional end condition Returns v, where v is the vector of iterations and n the number of iterations. See:

call (Euler Core)

functionsequence(f$:call, x:numerical, n:natural)

Computes sequences recursively. This function can be used to compute x[n]=f(x[1],...,x[n-1]) recursively. It will work for scalar x[i], or for column vectors x[i]. Depending on the iteration function, more than one start value x[1] to x[k] is needed. These start values are stored in a row vector x0 (or in a matrix for vector iteration). The iteration counter n can be used in the expression. f$ must be a function f$(x,n), or an expression or a call collection in x an n. The parameter x contains the values x[1],...,x[n-1] computed so far as elements of a row vector, or as columns in a matrix x. In case of a vector sequence, the function can also work with row vectors, depending on the result of the function f$. Note, that the start value must fit to the result of the function. f$ : function f$(x,n) where x is m x (n-1) or (n-1) x m, and the result m x 1 or 1 x m x : start values (m x k or k x m) n : desired number of values >sequence("x[n-1]+x[n-2]",[1,1],10) [1, 1, 2, 3, 5, 8, 13, 21, 34, 55] >sequence("n*x[n-1]",1,9) [1, 2, 6, 24, 120, 720, 5040, 40320, 362880] >A=[1,2;3,4]; sequence("A.x[,n-1]",[1;1],5) 1 3 17 91 489 1 7 37 199 1069 >matrixpower(A,4).[1;1] // more efficiently 489 1069 >function f(x,n) := mean(x[n-4:n-1]) >plot2d(sequence("f",1:4,20)): See:

iterate (Numerical Algorithms),

call (Euler Core)

functionsteffenson(f$:call, x:scalar, n:natural=0, eps=none)

Does n steps of the Steffenson operator, starting from x. This is a similar function as iterate. However, the function assumes an asymptotic expansion of the error of the convergence, and uses the Steffenson operator to speed up the convergence. f$ is either a function of a scalar returning a scalar, or a function of a column vector returning a column vector. f can also be an expression or a call collection of a variable x. f$ : function or expression of a scalar x x : start point n : optional number of iterations See:

iterate (Numerical Algorithms),

call (Euler Core)

functionnsteffenson(f$:call, x0:scalar, n:natural, eps=none)

Does n steps of the Steffenson operator, starting from x0. Works like "steffenson", but returns the history of the iteration. See:

niterate (Numerical Algorithms),

call (Euler Core)

functionaitkens(x: vector)

Improves the convergence of the sequence x. The Aitkens operator assumes that a-x[n] has a geometric convergence 1/c^n to 0. With this information, it is easy to compute the limit a. x : row vector (1xn, n>2) Return a row vector (1x(n-2)) with faster convergence. >v=iterate("cos",1,40); longest v[-1] 0.7390851699445544 >longest aitkens(v)[-1] 0.7390851332151597 >longest iterate("cos",1) 0.7390851332157367 See:

steffenson (Numerical Algorithms)

functionnewton(f$:call, df$:call, x:complex scalar, y:number=0, eps=none)

Solves f(x)=y with the Newton method. The Newton method needs the derivative of the function f. This derivative must be computed by the function df. The Newton method will start in x, and stop if the desired accuracy is reached. It is possible to set this accuracy with eps=... The function will only work for real or complex variables. For systems of equations, see "newton2". For intervals, see "inewton". f and df must be a function of a real or complex scalar, returning a scalar, or an expression or call collection of x. f : function or expression in x (real or complex, scalar) df : function or expression in x (real or complex, scalar) x : start value y : target value >newton("x^2","2*x",1,y=2) 1.41421356237 >function f(x) &= x^3-x+exp(x); >function df(x) &= diff(f(x),x); >newton(f,df,1), f(%) -1.13320353316 0 >newton(f,df,I), f(%) 0.351537+0.802637i 0+0i >function f(v) &= [v[1]+v[2]-1,v[1]^2+v[2]^2-10] >function Df(v) &= jacobian(f(v),[v[1],v[2]]) >newton2("f","Df",[4,3]), f(%) See:

secant (Numerical Algorithms),

bisect (Numerical Algorithms),

newton2 (Numerical Algorithms),

inewton (Numerical Algorithms),

mxminewton (Maxima Functions for Euler),

call (Euler Core)

functionnewton2(f$:call, df$:call, x:numerical, eps=none)

Newton method to solve a system of equations. The multidimensional Newton method can solve the equation f(v)=0 for vectors v. Here, f must map row vectors to row vectors. This function needs the Jacobian matrix of f$, which is provided through the function df. The Newton method will fail, if the Jacobian gets singular during the computation. f$ must be a function of a row vector, returning a row vector, or an expression of the row vector x. df must be a function of a row vector, returning the Jacobian matrix of f. Additional parameters after a semicolon are passed to the functions f and df. The function f can also use column vectors. In this case, the start value must be a column vector. f$ : function f$(v) (v: 1 x n or n x 1, result 1 x n or n x 1) df$ : function df$(v) (v: 1 x n or n x 1, result: n x n) x : start point (1xn or nx1) >function f(x) := [x[1]+x[2]-1,x[1]^2+x[2]^2-10] >function Df(x) := [1,1;2*x[1],2*x[2]] >newton2("f","Df",[4,3]), f(%) [2.67945, -1.67945] [0, 0] >function f([x,y]) &= [exp(x)-y,y-x-2]; >function Df([x,y]) &= jacobian(f(x,y),[x,y]); >newton2("f","Df",[1,1]), f(%) [1.14619, 3.14619] [0, 0] See:

broyden (Numerical Algorithms),

neldermin (Numerical Algorithms),

inewton2 (Numerical Algorithms),

call (Euler Core)

functionbroyden(f$:call, xstart:real, A:real=none, eps=none)

Finds a zero of f with the Broyden algorithm. The Broyden algorithm is an iterative algorithm just like the Newton method, which solves the equation f(v)=0 for vectors v. It tries to approximate the Jacobian of the function f using the previous iteration steps. The algorithm will fail, if the Jacobian of f in the zero is singular. The function f$ must take a vector v, and return a vector. Additional parameters after the semicolon are passed to f$. The function can work with column or row vectors. The start vector must be of the same form. f$ can be an expression or a call collection. To change the accuracy, you can specify an optional eps=... f$ : function of one vector xstart : start point, a row vector A : optional start for the Jacobian Returns the solution as a vector. See:

nbroyden (Numerical Algorithms),

call (Euler Core)

functionnbroyden(f$:call, xstart:real vector, nmax:natural, A:real=none, eps=none)

Do nmax steps of the Broyden algorithm. This function works like "broyden", but returns the steps of the algorithm. But it does at most nmax steps. f$ : function of one row vector, expression or call collection xstart : start point, a row vector nmax : maximal number of steps A : optional start for the Jacobian Returns a matrix, with one step of the algorithm in each row, and the current approximation of the Jacobian. See:

call (Euler Core)

DifMatrix:=%setupdif(5);

functiondiff(f$:call, x:numerical, n:natural=1, h:number=epsilon()^(1/3))

Compute the n-th (n<6) derivative of f in the points x. Numerical differentiation is inherently somewhat inaccurate for general functions. To get a good approximation, the first derivative uses 4 evaluations of the function. There is a more accurate function diffc() for functions, which are analytic and real valued on the real line. f is either a function in a scalar, or an expression or call collection in x. Additional parameters after a semicolon are passed to f. f$ : function or expression of a scalar x x : the point, where the derivative should be computed n : degree of derivative h : optional delta >diff("x^x",2) 6.77258872224 >&diff(x^x,x)(2) 6.77258872224 See:

diffc (Numerical Algorithms),

call (Euler Core)

functiondiffc(f$:call, x:numerical, h:number=epsilon())

Computes the first derivative for real analytic functions This uses the imaginary part of a f(x+ih)/h. Thus it must be possible to evaluate f into the complex plane. f$ : function or expression. The function must evaluate for complex values. h : step size See:

diff (Maxima Documentation),

call (Euler Core)

Most functions here are built-in functions. For an introduction to the Fourier Transform, see the following tutorial.

functioncomment overwrite fft(v:vector)

Fast Fourier Transform of v v : Real or complex row vector. It should have a number of columns with many low prime factors, like 2^n. This is the same as evaluating a polynomial with coefficients v in all roots of unity simultaneously, but much faster. v : If v is a matrix, the function returns the two dimensional FFT. In this case, the number of rows and columns of v must be a power of 2. >sec=1.2; t=soundsec(sec); s=sin(440*t)+cos(660*t)/2; >i=1:2000; plot2d(i/sec,abs(fft(s))[i]): See:

fht (Numerical Algorithms)

functioncomment overwrite ifft(v:vector)

Inverse Fast Fourier Transform of v The inverse of fft. See:

fft (Maxima Documentation)

functioncomment overwrite fht(v:real vector)

Fast Hartley Transform of v The Hartley transform is similar to the Fourier Transform, but works from real vectors to real vectors. It can be used for sound analysis in the same way the Fourier transform can be used. >sec=1.2; t=soundsec(sec); s=sin(440*t)+cos(660*t)/2; >i=1:2000; plot2d(i/sec,fht(s)[i]): Algorithm from AlgLib. See:

fft (Numerical Algorithms),

fft (Maxima Documentation)

functioncomment overwrite ifht(v:real vector)

Inverse Fast Hartley Transform of v The inverse of fht(). Algorithm from AlgLib.

functionfftfold(v:vector, w:vector)

fold v with w using the Fast Fourier Transformation. The length of the vector v should have small prime factors. For large v, this is much faster than fold(v,w). However, the vector v is folded as a periodic vector. To get the same result as fold(v,w), delete the first cols(w)-1 coefficients. v : row vector (1xn) w : row vector (1xm, m<=n) >fold(1:10,[1,2,1]/4) [2, 3, 4, 5, 6, 7, 8, 9] >tail(fftfold(1:10,[1,2,1]/4),3) [2, 3, 4, 5, 6, 7, 8, 9]

functioncomment overwrite fold(v:complex, w:complex)

fold v with w v and w can be real or complex vectors or matrices. In the result R, R[i,j] is the total sum of the pointwise multiplication of the submatrix of A with A[i,j] in the upper left corner and B. I.e. The result R is of size (cols(A)-cols(B)+1,rows(A)-rows(B)+1). Consequently B must be smaller in size than A. In the example, folding with [1/2,1/2] takes the average of two neighboring elements of 1:10. >fold(1:10,[0.5,0.5]) [ 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 ] For a matrix, folding with [1/4,1/4;1/4,1/4] takes the average of 2x2-sub-matrices. Folding with [1,-1] takes the difference of two elements, and is equivalent to differences. See:

fftfold (Numerical Algorithms)

functionoverwrite filter(b:complex vector, a:complex vector, .. x:complex vector, y:complex vector=none, zeros:integer=true)

Apply a filter with a and b to start values x and y Computes y[n] recursively using a[1]*y[n]+...+a[m]*y[n-m+1] = b[1]*x[n]+...+b[k]*x[n-k+1] a[1] must not be zero, of course. The start values on the right hand side are either 0 or the first k values of x, depending on the flag zeros (true by default). The start values on the left hand side are either 0 (the default), or the values of the parameter y. The size of a must exceed the size of x by 1. All input vectors must be real or complex row vectors or scalars. See:

fold (Numerical Algorithms),

fftfold (Numerical Algorithms)

Aliases to function in Euler Core

functioninterpolate(x:vector, y:vector)

Interpolate the values in y at x Alias to interp. Return the vector of divided differences. See:

interp (Euler Core),

evaldivdif (Numerical Algorithms)

functionevaldivdif(t:numerical, d:vector, x:vector)

Evaluate the divided differences d at t Works like interpval with other order of parameters. The first parameter contains the points, where the polynomial should be evaluated. Alias to interpval with t-argument first. Example: >xp=0:0.1:1; yp=sqrt(xp); >dp=divdif(xp,yp); >plot2d("sqrt(x)-evaldivdif(x,dp,xp)",0,1): See:

divdif (Euler Core)

functionevalpoly(t:numerical, p:vector)

evaluates a polynomial p in t. Polynomials are stored as a row vector with the constant coefficient in front. The function works for complex polynomials too. Alias to polyval with t-argument first. See:

polyval (Euler Core)

functionhermiteinterp(x:vector, y)

Divided differences for Hermite interpolation. x : row vector of interpolation points. x can contain double points in immediate succession. These points use derivatives for the interpolation. y : Either a function df(x,n), which computes the n-th derivatives of f (including the 0-th derivative), or a vector of values and derivatives in the form f(x),f'(x),f''(x),... for a multiple interpolation point x. Returns the divided differences, which can be used in interpval() (divdifeval()) as usual. The multiplicities in x can be generated with multdup(). >xp=multdup(1:3,2) [ 1 1 2 2 3 3 ] >yp=[1,0,0,0,-1,0] [ 1 0 0 0 -1 0 ] >d=hermiteinterp(xp,yp) [ 1 0 -1 2 -1.5 1.5 ] >plot2d("interpval(xp,d,x)",0.9,3.1); >function fh (x,n,f,fd) ... $ if n==0 then return f(x); $ elseif n==1 then return fd(x); $ else error("n=0 or n=1!"); $ endfunction >xp=multdup(chebzeros(0,1,5),2); >expr &= sqrt(x); >d=hermiteinterp(xp,"fh";expr,&diff(expr,x)); >plot2d("sqrt(x)-interpval(xp,d,x)",0,1); See:

divdif (Euler Core),

multdup (Numerical Algorithms)

functionhermitedivdif(x:vector, y)

Divided differences for Hermite interpolation. Alias to hermiteinterp() See:

hermiteinterp (Numerical Algorithms)

functionmultdup(x:numerical, n:nonnegative integer)

Repeat the rows or columns of x with multiplicities in n E.g., if n[1]=2, and n is a row vector, the first column of x is duplicated. If n is a column vector, the rows of x are duplicated, if it is a row vector, the columns are duplicated. n can be shorter than the number of rows resp. columns of x. If n is scalar, it acts on the rows, duplicating the first row n times. See:

dup (Euler Core),

hermite (Maxima Documentation)

functionremez(x:vector, y:vector, deg:index, tracing:integer=0, remezeps=0.00001)

Best polynomial approximation. The Remez algorithm computes the best polynomial approximation to the data (x[i],y[1]) of degree deg. This algorithm uses a simultaneous exchange, which requires the points x[i] to be sorted. x : vector of points on the real line, sorted. y : vector of values at these points. deg : degree of the polynomial. tracing : if non-zero, the steps will be plotted. Returns {t,d,h,r} t : the alternation points d : the divided difference form of the best approximation h : the discrete error (with sign) r : the rightmost alternation point, which is missing in t. To evaluate the polynomial in v use interpval(t,d,v). >x=equispace(-1,1,500); y=abs(x); >{t,d,h,r}=remez(x,y,10); >plot2d(x,interpval(t,d,x)-y): See:

interpval (Euler Core),

polyfit (Linear Algebra)

functionfmin(f$:call, a:real scalar, b:real scalar=none, d=0.01, dmax=1e20, eps=none)

Compute the minimum of the convex function f in [a,b]. Uses the golden cut method, starting with the interval [a,b]. The method takes about 60*(b-a) function evaluations for full accuracy. If no interval is given (b==none), the function searches left or right of a with initial step size d doubling d in each step until the function gets larger. Then the usual method is started. f$ is either an expression or call collection in x, or a function of x. Additional parameters are passed to a function f$. You can specify an epsilon eps with eps=... as last parameter. >fmin("x^x",0,1), 1/E 0.367879431154 0.367879441171 >function f(x) := x^x >f(fmin("f",0,1)) 0.692200627555 >fmin({{"x^2+x-a",a=2}},-2,2) -0.500000015805 >function f(x,a) := x^(a*x) >f(fmin("f",0,1;1.1),1.1) 0.667198694057 See:

fmax (Numerical Algorithms),

call (Euler Core)

functionfmax(f$:call, a:real scalar, b:real scalar=none, d=0.01, dmax=1e20, eps=none)

Compute the maximum of the concave function f in [a,b]. Works like fmin(). See:

fmin (Numerical Algorithms)

functionfextrema(f$:call, a:real scalar, b:real scalar, n:integer=100, eps=none)

Find all internal extremal points of f in [a,b]. f may be an expression or call collection in x or a function. Additional parameters after a semicolon are passed to a function f. f must vectorize to its first argument. n : number of sub-intervals to be scanned eps : epsilon for the search of the maximum Returns {minima,maxima} (vectors, possibly of length 0) See:

fmax (Numerical Algorithms),

fmin (Numerical Algorithms),

fzeros (Numerical Algorithms)

functionfzeros(f$:call, a:real scalar, b:real scalar, n:integer=100, eps=none)

Find all zeros of f in [a,b] f may be an expression or a call collection in x or a function. Additional parameters after a semicolon are passed to a function f. f must vectorize to its first argument The function uses fextrema() to find all local extreme values and uses bisectin() to find the zeros between sign changes. n : number of sub-intervals to be scanned eps : epsilon for the search of the maximum See:

fextrema (Numerical Algorithms)

functionbrentmin(f$:call, a:real scalar, d=0.1, eps=epsilon())

Compute the minimum of f around a with Brent's method. This function is no longer recommended. Use fmin() instead. d is an initial step size to look for a starting interval. eps is the final accuracy. Returns the point of minimal value. f is an expression or call collection in x, or a function in f. Return the point of minimum. >function f(x,a) := x^2+x-a >brentmin({{"f",2}},1;2) -0.499999992146 See:

fmin (Numerical Algorithms),

fmax (Numerical Algorithms),

call (Euler Core)

functionneldermin(f$:call, v:real, d=0.1, eps=epsilon(), tries=2)

Compute the minimum of f around v with the Nelder-Mead method. The Nelder-Mead method is a stable, but slow method to search for a local minimum of a function without using any derivative. It should not be used for high dimensions. Of course, it can be applied to solve a system of equations by minimizing the norm of the errors. This function cannot be used for one dimension. Use fmin() instead with a proper interval to search for the minimum. f must be function of a row or column vector x, returning a real value. Additional parameters after the semicolon are passed to f. f can also be an expression or a call collection depending on a vector x. d is an optional initial step size for a starting simplex. eps is the final accuracy. f : function f(v) (v : 1xn or nx1, result: scalar) v : start point for the search (1xn or nx1) d : optional size of start simplex eps : optional accuracy tries : number of restarts of the algorithm Return the point of minimum (1xn vector). >function f([x,y],a) &= x^2+y^2+exp(-x)+x*y 2 - x 2 y + x y + E + x >xmin=nelder({{"f",3}},[1,2]) [0.432563, -0.216282] >plot2d(&f(x,y,3),>contour); plot2d(xmin[1],xmin[2],>points,>add): See:

fmin (Numerical Algorithms),

nelder (Euler Core),

call (Euler Core)

functionoverwrite nelder(f$:call, v:real, d=0.1, eps=epsilon())

Compute the minimum of f around v with the Nelder-Mead method. For more explanations and examples, see neldermin(). See:

neldermin (Numerical Algorithms),

call (Euler Core)

functionnlfit(f$:call, Df$:call, v:real)

Minimizes f(x) from start vector v. This method is named after Levenberg-Marquardt. It minimizes a function from n variables to m variables (m>n) by minimizing the linear tangent function. If the norm gets larger it decreases the step size, until the step size gets closer than epsilon. While f can be an expression or a call collection, it is easier to use symbolic functions as in the example below. f(x) maps 1xn vectors to 1xm vectors (m>n) Df(x) is the Jacobian of f. >function f([x,y]) &= [x,y,x^2+2*y^2+1]; >function Df([x,y]) &= jacobian(f(x,y),[x,y]); >nlfit("f","Df",[0.2,0.1]) [-9.19403e-011, -1.07327e-010] >function h(v) := norm(f(v)) >nelder("h",[3,1]) // for comparison [6.33484e-007, -1.9733e-007] See:

nelder (Euler Core),

nelder (Numerical Algorithms),

call (Euler Core)

functionmodelfit(f$:call, param:vector, x:real, y:vector, powell=false, p=2, d=1e-2, eps=epsilon(), tries=2)

Fit a model f$ to data x,y with initial guess p This uses the Powell or the Nelder-Mead algorithm to fit a non-linear function to data x[i],y[1]. f$ : The model function f$(x,p). The function must vectorize to x. x will be a n x m vector for m data of n variables. The result of f$(x,p) must be a 1 x m vector. f$ can also be an expression or a call collection. x,y : The data to be used. x can be m x n, and y a 1 x n vector. param : Initial guess of the model parameter. x,y : Row vector of data. powell : If false, Nelder-Mead will be used. The following parameters are relevant for the Nelder-Mead method. Especially, decreasing d may help for difficult models. d : initial step size eps : accuracy tries : number of new starts >function model(x,p) := p[1]*cos(p[2]*x)+p[2]*sin(p[1]*x); >xdata = [-2,-1.64,-1.33,-0.7,0,0.45,1.2,1.64,2.32,2.9]; >ydata = [0.699369,0.700462,0.695354,1.03905,1.97389,2.41143, ... > 1.91091,0.919576,-0.730975,-1.42001]; >modelfit("model",[1,0.2],xdata,ydata) See:

neldermin (Numerical Algorithms),

powellmin (Numerical Algorithms),

call (Euler Core)

functionpowellmin(f$:call, x:real vector)

Minimize the function f with Powell's method f$ : function mapping a row vector to a real number x : start vector This function uses Powell's algorithm to find the minimum of a convex function. Make sure that the function is defined everywhere. You should return a large value outside the proper range of definition of f$. Alternatively, setting "errors off" will often work. Additional semicolon parameters are passed to f$. f$ can be a call collection or an expression in the vector xn. >function f([x,y]) := x^2+x*y+y^2+x >powellmin("f",[0,0]) [-0.666667, 0.333333] >function f(x,a) := x^2-a*x >powellmin({{"f",3}},1) 1.50000000147 >function f(x) := log(x)*x >errors off; powellmin("f",1) 0.367879439331 >errors on; See:

neldermin (Numerical Algorithms),

errors (Euler Core),

errors (Maxima Documentation)

functionbezier(x,y)

Compute the Bezier curve to points y in x The Bezier curve is the sum of y_i*B_i(t). The points y form the control polygon for the curve. The curve interpolates only the first and last point of y. In the following example, we take a square as the control polygon. Note that t runs from 0 to 1 along the curve. >y=[0,0,1,1;0,1,1,0]; >t=linspace(0,1,500); >plot2d(y[1],y[2],>addpoints); >p=bezier(t,y); >plot2d(p[1],p[2],>add): See:

spline (Numerical Algorithms)

functionspline(x,y)

Defines the natural Spline at points x[i] with values y[i]. The natural spline is the spline using cubic polynomials between the points x[i], smooth second derivative, and linear outside the interval x[1] to x[n]. The points x[i] must be sorted. The function returns the second derivatives at these points. With this information, the spline can be evaluated using "splineval". >xp=1:10; yp=intrandom(1,10,3); s=spline(xp,yp); >plot2d("splineval(x,xp,yp,s)",0,11); >plot2d(xp,yp,>points,>add); See:

splineval (Numerical Algorithms)

functionmap splineval(t:number; x:vector, y:vector, h:vector)

Evaluates the cubic interpolation spline for [x(i),y(i)] The function needs the second derivatives h[i] at t[i].

functionmap evalspline(t:number; x:vector, y:vector, s:vector)

Evaluates the cubic interpolation spline for (x(i),y(i)) with second derivatives s(i) at t See:

splineval (Numerical Algorithms)

functionmap linsplineval(x:number; xp:vector, yp:vector, constant:integer=1)

Evaluates the linear interpolating spline for (xp[i],yp[i]) at x xp must be sorted. Outside of the points of xp, the spline is continued as in the closest interval to x, or as a constant function, depending on the value of the parameter "constant". >xp=1:10; yp=intrandom(1,10,3); >plot2d("linsplineval(x,xp,yp,<constant)",0,11); >plot2d(xp,yp,>points,>add);

functionmap evallinspline(x:number; xp:vector, yp:vector, constant:integer=1)

Evaluates the linear interpolating spline for (xp[i],yp[i]) at x See:

linsplineval (Numerical Algorithms)

functionnspline(x:real, k:column integer, n:scalar integer, t:real vector)

Compute the normalized B-spline on t[k,k+n+1] in x. This spline is the normalized B-spline on the interval (t[k],t[k+n+1]) and has knots t[k] to t[k+n+1]. The polynomial degree is n. The spline is evaluated in the point x. This function maps to vectors x, but not to k and n. Examples: >plot2d("nspline";1,2,[1,2,3,4],a=1,b=4): >plot2d("nspline";(1:3)',2,[1,2,3,4,5,6],a=1,b=6):

There are many adaptive functions in EMT for integration and for differential equations. Moreover, the plot2d() function evaluates a function adaptively by default using adaptiveeval() and adaptiveevalone(). The function adaptive() is for adaptive evaluation of a general function.

functionadaptive(f$:call, a:number, b:number, eps=0.01, dmin=1e-5, dmax=1e5, dinit=0.1, factor=2)

Compute f$(x) for x in [a,b] with adaptive step size. The adaptive evaluation takes step sizes of different sizes to be able to return the values of the function with spacing not exceeding a give accuracy. The function is evaluated on an interval, with real or complex scalar or vector values. f$ is an expressions in x, a function or a call collection. The function must return a real or complex scalar or a column vector. eps is the target accuracy. a,b are the interval limits dmin and dmax are the minimal and maximal step size. dinit is the initial step size. factor is the factor the step size is changed with. Returns {x,y} x are the points of evaluation, and y is a row vector of values (or a matrix with columns of vector values). >{x,y}=adaptive("sqrt(x)*exp(-x)",0,10); plot2d(x,y): >plot2d(differences(x),>logplot): >{x,y}=adaptive("x*sin(1/x)",1,epsilon); plot2d(x,y): >function f(t:number,a:vector) := sum(exp(a*I*t)); >{t,z}=adaptive({{"f",[1,2,5]}},0,2*pi); plot2d(z,r=3): See:

adaptiveeval (Plot Functions)

Numerical integration in EMT can be done with various methods. The most accurate methods are the adaptive methods adaptivegauss() or alintegrate(). Less accurate, but fast methods are the Gauss quadrature or the LSODA integration. The function integrate defaults to the adaptive Gauss method.

functionargs simpson(f$:call, a:number, b:number, n:natural=50, maps:integer=0, allvalues=0)

Integrates f in [a,b] using the Simpson method The Simpson method is stable, but not fast. The results are not accurate. The method is exact for polynomials of degree 3. Use gauss() for better results. f is either a function of one real variable, or an expression in x. Additional parameters after a semicolon will be passed to the function f. f : function or expression in x a,b : interval bounds n : number of points to use maps : flag, if the expression needs to be mapped (0 or 1). Functions are always mapped. See:

romberg (Numerical Algorithms),

romberg (Maxima Documentation),

gauss (Numerical Algorithms)

functionsimpsonfactors(n)

Returns the vector 1,4,2,...,2,4,1.

functionromberg(f$:call, a:number, b:number, m:natural=10, maps:integer=0, eps=none)

Romberg integral of f in [a,b] This method gets very accurate results for many functions. The Romberg algorithm uses an extrapolation of the trapezoidal rule for decreasing step sizes. However, the Gauss method is to be preferred for functions, which are close to polynomials. The function may fail, if f is not smooth enough. f is either a function of one real variable, or an expression in x. Additional parameters after a semicolon will be passed to the function f. f : function or expression in x a,b : interval bounds m : number of points to use for the start of the algorithm maps : Flag, if the expression needs to be mapped. Functions are always mapped. >romberg("x^x",0,1) 0.783430510714 See:

simpson (Numerical Algorithms),

gauss (Numerical Algorithms),

integrate (Numerical Algorithms),

integrate (Maxima Documentation)

functionadaptiveint(f$:call, a:real scalar, b:real scalar, .. eps=epsilon(), steps=10)

Returns the integral from a to b with the adaptive Runge method. f is an expression in x, which must not contain y, or a function in x. Make sure, that f is computed at each point with relative error eps or less. Additional parameters after a semicolon are passed to a function f. f : function or expression in x a,b : interval bounds eps : accuracy of the adaptive process steps : hint for a good step size >adaptiveint("x^x",0,1) 0.783430510714 See:

adaptiverunge (Numerical Algorithms),

integrate (Numerical Algorithms),

integrate (Maxima Documentation)

functionadaptiveintlsoda(f$:call, a:real scalar, b:real scalar, .. eps=epsilon(), steps=10)

Returns the integral from a to b with the adaptive Runge method. f is an expression in x, which must not contain y, or a function in x. Make sure, that f is computed at each point with relative error eps or less. Additional parameters after a semicolon are passed to a function f. f : function or expression in x a,b : interval bounds eps : accuracy of the adaptive process steps : hint for a good step size See:

adaptiverunge (Numerical Algorithms),

integrate (Numerical Algorithms),

integrate (Maxima Documentation)

functionoverwrite alintegrate(f$:call, a:number, b:number)

Adaptive integration Algorithm from AlgLib similar to adaptivegauss(). See:

integrate (Maxima Documentation)

functionoverwrite alsingular(f$:call, a:number, b:number, alpha:number, beta:number)

Adaptive integration Algorithm from AlgLib to integrate a function with singularities at the boundaries. The singularities must be of type (x-a)^alpha and (b-x)^beta. Either alpha or beta can be 0, which denotes a smooth function. >alsingular("1/sqrt(x)",0,1,-0.5,0) 2 See:

integrate (Numerical Algorithms),

integrate (Maxima Documentation),

alintegrate (Numerical Algorithms)

functionintegrate(f$:call, a:real scalar, b:real scalar, .. eps=epsilon(), steps=10, method:integer=0, fast:integer=0, maps:integer=1)

Integrates f from a to b with the adaptive Runge method. Calls an integration method, depending on the method parameter. The default is the Gauss integration with 10 subintervals. Available is the adaptive Runge, Gauss and the LSODA method. Note, that the fastest algorithm is the Gauss algorithm, which is the method of choice. method : 0=adaptive Gauss, (needs vectorized function) 1=adaptive Runge, 2=lsoda, 3=AlgLib fast : Simple Gauss with one sub-interval (needs vectorized function) >integrate("x^x",0,1) 0.783430510712 See:

mxmiint (Maxima Functions for Euler),

gauss (Numerical Algorithms),

adaptivegauss (Numerical Algorithms),

alintegrate (Numerical Algorithms),

lsoda (Numerical Algorithms)

functionlegendre(n:index)

Compute the coefficients of the n-th Legendre polynomial. This is used to compute the Gauss coefficients. See:

gauss (Numerical Algorithms)

functiongaussparam(n:index)

Returns the knots and alphas of gauss integration at n points in [-1,1]. Returns {gaussz,gaussa}.

functiongauss(f$:call, a:real scalar, b:real scalar, n:index=1, .. maps:integer=1, xn=gaussz, al=gaussa)

Gauss integration with 10 knots and n subintervals. This function is exact for polynomials up to degree 19, even for one sub-interval. For other functions, it may be necessary to specify n subintervals. f is an expression in x, or a function in x. Additional parameters after the semicolon are passed to f. maps : Vectorize an expression to the arguments. On by default. Turn off for a bit more performance. See:

gauss5 (Numerical Algorithms),

simpson (Numerical Algorithms),

integrate (Numerical Algorithms),

integrate (Maxima Documentation),

romberg (Numerical Algorithms),

romberg (Maxima Documentation)

functionigauss(expr$:call, a:real, b:real, n:index=1, .. dexpr20$:string=none, dexpr20est:real=none, xn=gaussz, al=gaussa)

Compute an interval inclusion for an integral This works only for expressions, unless you provide an estimate for the 20-th derivative. The function will compute this derivative with Maxima otherwise. Optional: dexpr20: Expression or function for the 20-th derivative dexpr20est: Interval inclusion of the 20-th derivative >igauss("exp(-x^2)",0,1) ~0.7468241328124256,0.7468241328124285~ See:

gauss5 (Numerical Algorithms)

functiongauss5(f$:call, a:real scalar, b:real scalar, n:index=1, .. maps:integer=0, xn=gauss5z, al=gauss5a)

Gauss integration with 4 knots and n subintervals This is exact for polynomials to degree 7 (even for n=1). The results are usually much better than the Simpson method with only twice as many function evaluations. See:

simpson (Numerical Algorithms),

integrate (Numerical Algorithms),

integrate (Maxima Documentation),

romberg (Numerical Algorithms),

romberg (Maxima Documentation)

functiongaussfxy(f$:call, a:real scalar, b:real scalar, .. c:real scalar, d:real scalar, n:index=1)

Computes the double integral of f on [a,b]x[c,d]. This function uses Gauss integration for the inner and the outer integral. The function is exact for polynomials in x and y up to degree 19. f$ : a function f(x,y) or an expression of x and y. >gaussfxy("exp(x)*y+y^2*x",0,1,0,2) 4.76989699025 >&integrate(integrate(exp(x)*y+y^2*x,x,0,1),y,0,2)() // exact! 4.76989699025

functionadaptivegauss(f$:call, a:real scalar, b:real scalar, .. eps=epsilon*100, n=1, maps:integer=1)

Adaptive Gauss integral of f on [a,b]. This function divides the interval into subintervals, and tries a Gauss integration with 1 and 2 intervals on each sub-interval. If both do not agree good enough the step size is divided by 2. the algorithm tries to double the step size in each step.. n : default number of subintervals eps : default accuracy maps : Vectorizes the call to f$. Turn off for more performance. Examples: adaptivegauss("sqrt(x)",0,1)

The LSODA method should be the method of choice. It is a very efficient adaptive method for ordinary differential equations, which takes also care of stiff cases. But the Heun and Runge methods are available too, including an adaptive Runge method, which can be used for adaptive integration.

Vector fields can be plotted using the vectorfield() function in plot2d.

There are also the interval algorithms mxmidlg() and mxmiint().

Numerical Solutions of Differential Equations

functionheun(f$:string, t:real vector, y0:real vector)

Solves the differential equation y'=f(x,y) at x=t with y(t[1])=y0. This function works also for systems of n differential equations. f must be a function with two parameters f(x,y), where x is scalar, and y is scalar or a 1xn row vector in the case of a system of differential equations. f must return a scalar, or a 1xn row vector respectively. Additional parameters for "heun" after a semicolon are passed to a function f. Alternatively, f can be an expression in x and y. y0 is the initial value y(t[1]), a scalar, or a 1xn row vector. t is the 1xm row vector of points, where the differential equation will be solved. f : function or expression in x and y t : row vector of points, where y(t) should be computed y0 : initial value, scalar or row vector The function returns a 1xm row vector of y, or a nxm matrix in the case of a system of n equations. >x=0:0.01:2; plot2d(x,heun("-2*x*y",x,1)): >function f(x,y,a) &= -a*x*y; >heun("f",x,1;2)[-1] 0.0183155649842 >exp(-4) 0.0183156388887 See:

runge (Numerical Algorithms)

functionrunge(f$:string, t:real vector, y0:real, .. steps:index=1)

Solves the differential equation y'=f(x,y) at x=t with y(t[1])=y0. This function works also for systems of n differential equations. f must be a function with two parameters f(x,y), where x is scalar, and y is scalar or a vector in the case of a system of differential equations. f must return a scalar, or a vector respectively. Additional parameters for "runge" after a semicolon are passed to a function f. Alternatively, f can be an expression in x and y. y0 is the initial value y(t[1]), a scalar, or a 1xn row vector. t is the 1xm vector of points, where the differential equation will be solved. steps are optional intermediate steps between the t[i]. This parameter allows to take extra steps, which will not be stored into the solution. Note that f can work with row or column vectors, but the initial value must be of this form too. The return matrix will always consist of column vectors. f : function or expression in x and y t : row vector of points, where y(t) should be computed y0 : initial value, scalar or row vector steps : additional steps between the points of x Returns a row vector y(t[i]) for the elements of t. Im case of a system, the function returns a matrix with columns y(t[i]). >x=0:0.01:2; plot2d(x,runge("-2*x*y",x,1)): >function f(x,y,a) &= -a*x*y; >runge("f",x,1;2)[-1] 0.018315639424 >exp(-4) 0.0183156388887 See:

heun (Numerical Algorithms),

adaptiverunge (Numerical Algorithms)

functionadaptiverunge(f$:call, x:real vector, y0:real, .. eps=epsilon(), initialstep=0.1)

Solves the differential equation y'=f(x,y) at x=t with y(t[1])=y0 adaptively. The function uses an adaptive step size between x[i] and x[i+1] to guarantee an accuracy eps. By default the accuracy is the default epsilon, but eps=... can be specified as an extra parameter. This function works also for systems of n differential equations. f must be a function with two parameters f(x,y), where x is scalar, and y is scalar or a vector in the case of a system of differential equations. f must return a scalar, or a vector respectively. Additional parameters for "adaptiverunge" after a semicolon are passed to a function f. Alternatively, f can be an expression in x and y. y0 is the initial value y(t[1]), a scalar or a vector. t is the 1xm vector of points, where the differential equation will be solved. Note that f can work with row or column vectors, but the initial value must be of this form too. The return matrix will always consist of column vectors. f : function or expression in x and y t : row vector of points, where y(t) should be computed y0 : initial value, scalar or row vector eps : accuracy of the adaptive method initialstep : initial step size Returns a row vector y(t[i]) for the elements of t. Im case of a system, the function returns a matrix with columns y(t[i]). >longest adaptiverunge("-2*x*y",[0,2],1) 1 0.01831563888880886 >longest E^-4 0.01831563888873419 See:

heun (Numerical Algorithms)

functionode(f$:call, t:real vector, y:real, .. eps:real=epsilon(), warning:integer=false, reset:integer=false)

Solves the differential equation y'=f(x,y) at x=t with y(t[1])=y0. This function works also for systems of n differential equations. The algorithm used is the LSODA algorithm for stiff equations. This algorithm switches between stiff and normal case automatically. The LSODA algorithm is based on work by Linda R. Petzold and Alan C. Hindmarsh, Livermore National Laboratory. The EMT version is based on a C source by Heng Li, MIT. f must be a function with two parameters f(x,y), where x is scalar, and y is scalar or a vector in the case of a system of differential equations. f must return a scalar, or a vector respectively. Additional parameters for "lsoda" after a semicolon are passed to a function f. Alternatively, f can be an expression or a call collection in x and y. This is an adaptive algorithm. But for some functions, it is necessary to add intermediate points, especially if the function is close to 0 over wide subintervals. Otherwise, it has been observed that the algorithm fails. If you are sure that the problem does not fall under the problematic category, you can leave reset=false, which will interpolate intermediate points instead of resetting the algorithm every time. This is a huge benefit in terms of function evaluations. By default, reset is false. y0 is the initial value y(t[1]), a scalar, or a 1xn row vector. t is the 1xm vector of points, where the differential equation will be solved. Note that f can work with row or column vectors, but the initial value must be of this form too. The return matrix will always consist of column vectors. f : function or expression in x and y t : row vector of points, where y(t) should be computed y0 : initial value, scalar or row vector warning : toggles warnings from lsoda (but not errors) reset : reset the integration between iterations Returns a row vector y(t[i]) for the elements of t. Im case of a system, the function returns a matrix with columns y(t[i]). >function f(x,y,a) &= -a*x*y; >ode({{"f",2}},x,1)[-1] 0.0183156388888 >exp(-4) 0.0183156388887 >ode("x*y",[0:0.1:1],1)[-1], exp(1/2) // solve y'=x*y 1.64872127078 1.6487212707 >function f(x,y) := -x*y^2/(y^2+1); // y'=-xy/(y^2+1) >t=linspace(0,5,1000); plot2d(t,ode("f",t,1)): >sol &= ode2('diff(y,x)=-x*y^2/(1+y^2),y,x) // check with Maxima >&solve(sol with %c=0,y)[2], plot2d(&rhs(%),0,5): >function f(x,y) := [y[2],-10*sin(y[1])]; // y''=-10*sin(y) >t=linspace(0,2pi,1000); plot2d(t,ode("f",t,[0,1])[1]): See:

heun (Numerical Algorithms),

runge (Numerical Algorithms),

adaptiverunge (Numerical Algorithms),

lsoda (Numerical Algorithms),

call (Euler Core)

functionoverwrite lsoda(f$:call, t:real vector, y:real, .. eps:real=epsilon(), warning:integer=true, reset:integer=false)

Solves the differential equation y'=f(x,y) at x=t with y(t[1])=y0. See:

ode (Numerical Algorithms)

Euler has support for a compressed format for thin matrices. This format stores non-zero elements only. Operations on such matrices are typically very much faster. For examples see the following introduction.

To solve large, sparse equations the CG-method implemented in cgX() or cpxfit() is the method of choice. The function cpxfit() uses the normal equation to fit Ax-b. The Gauss-Seidel method seidelX() is an alternative for diagonal dominant matrices.

functioncomment cpx(A)

Compress the matrix A The compressed matrix stores only the non-zero values of A. It consists of lines of the form (i,j,x), which means A[i,j]=x. Note that compressed matrices are a separate data type in Euler. >cpx(id(3)) Compressed 3x3 matrix 1 1 1 2 2 1 3 3 1 See:

decpx (Numerical Algorithms)

functioncomment decpx(X)

Decompress the compressed matrix X >C=cpxzeros(3,3) Compressed 3x3 matrix >decpx(cpxset(C,[1,1,0.5;2,3,0.7])) 0.5 0 0 0 0 0.7 0 0 0

functioncomment cpxzeros([n,m])

Empty compressed matrix of size nxm

functioncomment cpxset(X,K)

Set elements in the compressed matrix X to K is a matrix containing lines of type (i,j,x). Then X[i,j] will be set to x. If the element already exists in X, it is replaced by the new value. >function CI(n) := cpxset(cpxzeros(n,n),(1:n)'|(1:n)'|1) >decpx(CI(3)) 1 0 0 0 1 0 0 0 1 See:

decpx (Numerical Algorithms),

cpxget (Numerical Algorithms)

functioncomment cpxget(X)

Get the elements of a compressed matrix X The return value is a matrix containing lines of type (i,j,x). >H=[1,1,0.1;2,3,0.7] 1 1 0.1 2 3 0.7 >cpxget(cpxset(cpxzeros(3,3),H)) 1 1 0.1 2 3 0.7 See:

cpxset (Numerical Algorithms)

functioncomment cpxmult(A,B)

Multiply two compressed matrices This is a fast algorithm to multiply two compressed, sparse matrices. The result is a compressed matrix.

functioncomment cpxseidel(C,b,x,om)

One step of the Gauss-Seidel algorithm for Cx=b. C must be a compressed matrix, x is the step to go from, and om is the relaxation parameter. See:

seidelX (Numerical Algorithms),

cgX (Numerical Algorithms)

functionseidelX(H:cpx, b:real column, x:column=0, .. om:real positive scalar=1.2, eps=none)

Solve Hx=b using the Gauss-Seidel method for compressed matrices H. The Gauss-Seidel method with Relaxation is an iterative method, which converges for all positive definite matrices. For large matrices, it may work well. However, the conjugate gradient method "cgX" is the method of choice. H must be diagonal dominant, at least not have 0 on the diagonal. om is the relaxation parameter between 1 and 2. x is start value (automatic if 0). It is possible to specify the accuracy with eps=... H : compressed matrix (nxm) b : column vector (mx1) om : optional relaxation coefficient Returns the solution x, a column vector. See:

cgX (Numerical Algorithms),

seidel (Linear Algebra)

functioncgX(H:cpx, b:real column, x0:real column=none, f:index=10, eps=none)

Conjugate gradient method to solve Hx=b for compressed H. This is the method of choice for large, sparse matrices. In most cases, it will work well, fast, and accurate. H must be positive definite. Use cpxfit(), if it is not. The accuracy can be controlled with an additional parameter eps. The algorithm stops, when the error gets smaller then eps, or after f*n iterations, if the error gets larger. x0 is an optional start vector. H : compressed matrix (nxm) b : column vector (mx1) x0 : optional start point (mx1) f : number of steps, when the method should be restarted >X=cpxsetdiag(cpxzeros(1000,1000),0,2); >X=cpxsetdiag(cpxsetdiag(X,-1,1),1,1); >b=random(1000,1); >x=cgX(X,b); >totalmax(abs(cpxmult(X,x)-b)) 0 See:

cpxfit (Numerical Algorithms),

cg (Linear Algebra),

cgXnormal (Numerical Algorithms)

functioncgXnormal(H:cpx, Ht:cpx, b:real column, x0:real column=none, .. f:index=10, eps=none)

Conjugate gradient method to solve Ht.H.x=Ht.b for compressed H. This algorithm is used by cpxfit() to solve the normal equation H'Hx=H'b, which minimizes |Hx-b|, i.e., to find an optimal solution of an linear system with more equations than unknowns. Stops, when the error gets smaller then eps, or after f*n iterations, when the error gets larger. x0 is an optional start vector. H : compressed matrix (nxm) Ht : compressed matrix (mxn) b : column vector (mx1) x0 : optional start (mx1) f : number of steps, when the method should be restarted See:

cpxfit (Numerical Algorithms)

functioncpxfit(H:cpx, b:real column, f:index=10, eps=none)

Minimize |Hx-b| for a compressed matrix H. This function uses the conjugate gradient method to solve the normal equation H'Hx=H'b for sparse compressed matrices H. H : compressed matrix (nxm) b : column vector (mx1) f : number of steps, when the method should be restarted >X=cpxsetdiag(cpxzeros(1000,1000),0,2); >X=cpxsetdiag(X,1,1); >b=random(1000,1); >x=cpxfit(X,b); >totalmax(abs(cpxmult(X,x)-b)) 0 See:

fit (Linear Algebra),

fitnormal (Linear Algebra),

svdsolve (Linear Algebra)

functioncpxsetdiag(R:cpx, k:integer scalar, d:real vector)

Set the k-th diagonal of the compressed matrix R to the value d. k=0 is the main diagonal, k=-1 the diagonal below, and k=1 the diagonal above. Note that this function does not change R, but returns a new matrix with the changes. >function CI(n) := cpxsetdiag(cpxzeros(n,n),0,1); >decpx(CI(3)) 1 0 0 0 1 0 0 0 1 See:

setdiag (Euler Core)

functioncpxmultrows(c:real column, A:cpx)

Multiply the i-th row of the compressed matrix A by c[i]. Note that this function does not change R, but returns a new matrix with the changes. c : column vector (nx1) A : compressed matrix (nxm) >function CI(n) := cpxsetdiag(cpxzeros(n,n),0,1); >decpx(cpxmultrows((1:3)',CI(3))) 1 0 0 0 2 0 0 0 3 See:

cpxsetrow (Numerical Algorithms)

functioncpxsetrow(A:cpx, i:index, r:real vector)

Set the i-th row of the compressed matrix A to r. Note that this function does not change R, but returns a new matrix with the changes. A : compressed matrix (nxm) i : index r : row vector (1xm)

functioncpxsetcolumn(A:cpx, j:index, c:real column)

Set the j-th column of the compressed matrix A to c. Note that this function does not change R, but returns a new matrix with the changes. A : compressed matrix (nxm) j : integer c : column vector (mx1) See:

cpxsetrow (Numerical Algorithms)

functioncpxsum(A:cpx)

The sums of all rows of the compressed matrix A. See:

sum (Euler Core),

sum (Maxima Documentation)

functionrectangleIncidenceX(n:index, m:index)

Incidence matrix of a rectangle grid in compact form. The incidence matrix of a graph is the matrix H, such that H(i,j) contains 1, if node i is connected to node j. In this function, the graph consists of the points of a rectangle, and the edges connect adjacent points. The points in the rectangle are numbered row by row. Returns a compressed nm x nm matrix. See:

cpxset (Numerical Algorithms)

Euler has a long accumulator, which can compute the scalar product and the residuum of a linear equation exactly. On this basis, Euler implements a residuum iteration to solve linear systems. It can be used to invert matrices more exactly, or to evaluate polynomials.

Residuum iterations uses the long accumulator of Euler to compute an exact scalar product.

functionxlgs(A:complex, b:complex, n:integer=20, eps=none)

Compute a more exact solution of Ax=b using residuum iteration. You can specify the relative accuracy with eps=... This epsilon is used to determine, if the algorithm should stop. n : the maximal number of residual iterations. >H=hilbert(10); >longest totalmax(abs(xlgs(H,sum(H))-1)) 0 >longest totalmax(abs(H\sum(H)-1)) 8.198655030211555e-005 See:

ilgs (Numerical Algorithms)

functionxinv(A:complex, n:integer=20, eps=none)

Compute the inverse of A using residuum iteration. You can specify the relative accuracy with eps=... as last parameter. Additionally, a maximal number of iteration n can be set. See:

xlgs (Numerical Algorithms)

functionxlusolve(A:complex, b:complex, n:integer=20, eps=none)

Compute the solution of Ax=b for L- or U-matrices A. Works for lower triangle matrices with diagonal 1, or for upper triangle matrices. The function is just a faster version of xlgs. You can specify the relative accuracy with eps=... as last parameter. Additionally, a maximal number of iteration n can be set. See:

xlgs (Numerical Algorithms),

lu (Linear Algebra),

lusolve (Linear Algebra)

functionxpolyval(p:complex vector, t:complex, n:integer=20, eps=none)

Evaluate the polynomial at values t using residuum iteration. Alias to xevalpoly(). See:

xevalpoly (Numerical Algorithms)

functionxevalpoly(t:complex, p:complex vector, n:integer=20, eps=none)

Evaluate the polynomial at values t using residuum iteration. You can specify the relative accuracy with eps=... as last parameter. Additionally, a maximal number of iteration n can be set. >p:=[-945804881,1753426039,-1083557822,223200658]; ... >t:=linspace(1.61801916,1.61801917,100); ... >plot2d(t-1.61801916,evalpoly(t,p)): >plot2d(t-1.61801916,xevalpoly(t,p,eps=1e-17)): See:

xlgs (Numerical Algorithms)

functionxeigenvalue(a,l,x=none)

Returns an improved eigenvalue of A, starting with the approximation l. l must be close to a simple eigenvalue, and x close to an eigenvector, if x is not 0. This is the inverse iteration due to Wielandt. Returns the eigenvalue and the eigenvector. >H=hilbert(10); >lambda=jacobi(H)[1] 255023613680 >lambda=lambda+1000; >x=eigenspace(H,lambda); >norm(H.x-lambda*x) 7445.4368515 >{lambda,x}=xeigenvalue(H,lambda,x); >norm(H.x-lambda*x) 3.7570410926e-005 See:

eigenvalues (Linear Algebra),

eigenvalues (Maxima Documentation)

functionieval(f$:string, x:interval scalar, n:integer=10)

Better evaluation of the expression in f for the interval x. Evaluating an expression directly assumes that all values in all intervals are combined. If the expression is a function of x, this is not the way we want to to evaluate the expression. Then we want to take the same point whenever x occurs in the expression. So the interval is split into subintervals for more accuracy, and then the usual evaluation takes place in each subinterval. The results are combined. n : number of subintervals. >expr &= x^3-x+1/x^2; plot2d(expr,0.8,0.9): >x=~0.8,0.9~; expr() ~0.84,1.5~ >ieval(expr,~0.8,0.9~) // better ~0.99,1.3~ >ieval(&diff(expr,x),~0.8,0.9~)<0 // decreasing function 1 >longest expr(~0.8~)||expr(~0.9~) // correct ~1.063,1.275~ >function f(x) := @expr >ieval("f",x) // the same with functions ~0.99,1.3~ See:

mxmieval (Maxima Functions for Euler),

ievalder (Numerical Algorithms)

functionievalder(f$:string, fd$:string, xi:interval scalar, n:integer=10)

Better evaluation of the expression in f for the interval x. The derivative is used to improve the interval accuracy. The interval is split into sub-intervals for more accuracy. See:

mxmieval (Maxima Functions for Euler)

functionidgl(f$:string, x:real vector, y0:interval scalar)

Guaranteed inclusion of y'=f(t,y0;...) at points t with y(t[1])=y0. This is a quick inclusion for a differential equation, which avoids the use of any Taylor series. The inclusion is very coarse, however. The function uses a simple Euler one step method. The result is an interval vector of values. See:

mxmidgl (Maxima Functions for Euler),

idglder (Numerical Algorithms)

functionidglder(f$:string, fx$:string, fy$:string, x:real vector, .. y0:interval scalar)

Inclusion for y'=f(t,y0;...) at t with y(t[1])=y0. This function needs the partial derivatives of f to x and y. The result is an interval vector of values. The result is a very coarse inclusion. For better results, see mxmidgl(). f, fx and fy are functions in f(x,y), or expressions of x and y. Additional arguments are passed to the functions. >function f(x,y) &= -x*sin(y); >function fx(x,y) &= diff(f(x,y),x); >function fy(x,y) &= diff(f(x,y),y); >x=0:0.01:2; >idglder("f","fx","fy",x,~1~)[-1] ~0.137,0.158~ >mxmidgl(&f(x,y),x,~1~)[-1] ~0.14759945743769,0.14759945743821~ See:

mxmidgl (Maxima Functions for Euler)

functionisimpson(f$:string, der$:string, a:number, b:number, .. n:index=50)

Interval Simpson integral of f from a to b. This function uses the Simpson method and its error estimate to get guaranteed inclusions of integrals. Other interval methods like igauss or mxmiint provide better estimates. f : expression (must map to arguments and work for intervals) der : expression for fourth derivative (like f) a,b : interval bounds n : number of subintervals >isimpson("exp(-x^2)",&diff(exp(-x^2),x,4),0,2) ~0.8820813879,0.8820813936~ >igauss("exp(-x^2)",0,2) ~0.88208139037,0.88208139115~ >mxmiint("exp(-x^2)",0,2) ~0.8820813907619,0.882081390763~ See:

mxmisimpson (Maxima Functions for Euler)

functionilgs(A:interval, b:interval, R="", steps=100)

Guaranteed interval inclusion for the solution of A.x=b. This function uses an interval algorithm, and an exact residuum calculation. If the algorithm succeeds, the result is a guaranteed inclusion for the solution of the linear system. Note that the algorithm can only work for regular A, or interval matrices not containing singular A. A and b may be of interval or real type. The optional R should be a pseudo inverse to A. >H=hilbert(10); b=sum(H); >longest max(diameter(ilgs(H,b)')) 1.695865670114927e-012 >longest max(abs(xlgs(H,b)'-1)) 0 >longest max(abs((H\b)'-1)) 8.198655030211555e-005 See:

xlgs (Numerical Algorithms)

functioniinv(A:interval)

Guaranteed interval inverse of the matrix A. See:

inv (Linear Algebra),

xinv (Numerical Algorithms)

functionievalpoly(t:interval, p:interval vector)

Guaranteed evaluation of a polynomial p(t). p contains the coefficients of a polynomial. Euler stores polynomials starting with the constant coefficient. See:

polyval (Euler Core),

xpolyval (Numerical Algorithms)

functionipolyval(p:interval vector, t:interval)

Guaranteed evaluation of a polynomial p(t). See:

ievalpoly (Numerical Algorithms)

functionibisect(f:string, a:scalar, b:scalar=none, y:scalar=0)

Interval bisection algorithm to solve f(x)=y >function f(x) &= x^2+x; >x=ibisect("f(x)",0,4,y=2) ~0.99999999999999967,1.0000000000000004~~ >x=ibisect("f(x)",0,4,y=~1.99,2.01~) ~0.9958,1.005~~ >function f(x,a) &= x^a-a^x; >ibisect("f",1,3;~1.999,2.001~) // a as semicolon parameter, y=0 ~1.98,2.02~~ See:

inewton (Numerical Algorithms)

functionibis(f,a,b=none,y=0) ... if b==none then b=right(a); a=left(a); endif;

functioninewton(f$:string, df$:string , xi: interval, yi:real scalar="", y=0)

Guaranteed interval inclusion of the zero of f. df must compute an inclusion of the derivative of f for intervals x. f and df must be functions of one scalar variable, or expressions in x. Additional parameters after the semicolon are passed to both functions. The initial interval x must already contain a zero. If x is a point, and not an interval, the function tries to get an initial interval with the usual Newton method. Returns {x0,f}: the solution and a flag, if the solution is guaranteed. >expr &= x^2*cos(x); >inewton(expr,&diff(expr,x),1,y=0.1) ~-0.32483576255267282,-0.32483576255267244~ >longest solve(expr,0.3,y=0.1) 0.3248357625526727 See:

inewton2 (Numerical Algorithms),

mxminewton (Maxima Functions for Euler),

inewton2 (Numerical Algorithms)

functioninewton2(f$:string, Df$:string, x:interval, check:integer=false)

Guaranteed inclusion of the zero of f, a function of several parameters. Works like newton2, starting from a interval vector x which already contains a solution. If x is no interval, the function tries to find such an interval. f and Df must be a function of a row vector x, or an expression in x. f must return a row vector, and Df the Jacobi matrix of f. Returns {x,valid}. If check is false, the result is not checked for a guaranteed inclusion. In this case the return value of valid can be checked to learn, if the inclusion is a guaranteed inclusion. If checked is true valid=0 will throw an error exception.

functionplotintervals(r, style=none)

Adds plots of two dimensional intervals to a given plot. r is an nx2 vector of intervals containing the x values in the first row, and the y values in the second row. Each value is plotted as a bar. plot2d() can also plot (x,y), where y is a vector of intervals. But there the values of y are assumed to be the result of a function with errors. >expr &= x^3-x; >plot2d(expr,r=1.1); >x=(-1:0.1:1)' + ~-0.02,0.02~; >plotintervals(x|expr(x),style="O"): See:

mxmibisectfxy (Maxima Functions for Euler)

functionarnoldi(A, q ,m)

Compute an orthonormal basis of the Krylov space This function computes a matrix Q with orthonormal columns which are a basis of the Krylov matrix b, Ab, A^2.b, ..., A^m.b A : real nxk matrix q : real nx1 vector m : integer Result Q : real nx(m+1) matrix n=6; A=normal(n,n); q=normal(n,1); ... Q = arnoldi(A,q,4) Q'.Q // -> id(5) Q'.A.Q // -> Hessenberg matrix