Features of Nonlinear Solvers

Functions (135 items):

BDQRF("1:function", "2:condition", "3:condition") — Bisected Direct Quadratic Regula Falsi root-finding method of function "1:function", giving a couple of delimiters "2:condition" and "3:condition"; calculation have at least 4 decimal places function precision.
BDQRF("1:function", "2:condition", "3:condition", "4:condition") — Bisected Direct Quadratic Regula Falsi root-finding method of function "1:function", giving a couple of delimiters "2:condition" and "3:condition"; calculation have at least "4:condition" function precision.
BDQRF("1:function", "2:condition", "3:condition", "4:condition", "5:condition") — Bisected Direct Quadratic Regula Falsi root-finding method of function "1:function", giving a couple of delimiters "2:condition" and "3:condition"; calculation have at least "4:condition" function precision or "5:condition" variable precision.
BDQRF("1:function", "2:condition", "3:condition", "4:condition", "5:condition", "6:number", "7:variable", "8:variable", "9:variable") — Bisected Direct Quadratic Regula Falsi root-finding method of function "1:function", giving a couple of delimiters "2:condition" and "3:condition"; calculation have at least "4:condition" function precision or "5:condition" variable precision. A "6:number" different from 0 set your custom max number of iterations, a "7:variable" different from 0 show you the number of iterations, a "8:variable" different from 0 show you a step-by-step summary and a "9:variable" different from 0 save a CSV summary into the current working directory.
Bisection("1:function", "2:condition", "3:condition") — Bisection root-finding method of function "1:function", giving a couple of delimiters "2:condition" and "3:condition"; calculation have at least 4 decimal places function precision.
Bisection("1:function", "2:condition", "3:condition", "4:condition") — Bisection root-finding method of function "1:function", giving a couple of delimiters "2:condition" and "3:condition"; calculation have at least "4:condition" function precision.
Bisection("1:function", "2:condition", "3:condition", "4:condition", "5:condition") — Bisection root-finding method of function "1:function", giving a couple of delimiters "2:condition" and "3:condition"; calculation have at least "4:condition" function precision or "5:condition" variable precision.
Bisection("1:function", "2:condition", "3:condition", "4:condition", "5:condition", "6:variable", "7:variable", "8:variable") — Bisection root-finding method of function "1:function", giving a couple of delimiters "2:condition" and "3:condition"; calculation have at least "4:condition" function precision or "5:condition" variable precision. A "6:variable" different from 0 show you the number of iterations, a "7:variable" different from 0 show you a step-by-step summary and a "8:variable" different from 0 save a CSV summary into the current working directory.
Brent("1:function", "2:condition", "3:condition") — Brent's root-finding method of function "1:function", giving a couple of delimiters "2:condition" and "3:condition"; calculation have at least 4 decimal places function precision.
Brent("1:function", "2:condition", "3:condition", "4:condition") — Brent's root-finding method of function "1:function", giving a couple of delimiters "2:condition" and "3:condition"; calculation have at least "4:condition" function precision.
Brent("1:function", "2:condition", "3:condition", "4:condition", "5:condition") — Brent's root-finding method of function "1:function", giving a couple of delimiters "2:condition" and "3:condition"; calculation have at least "4:condition" function precision or "5:condition" variable precision.
Brent("1:function", "2:condition", "3:condition", "4:condition", "5:condition", "6:number", "7:variable", "8:variable", "9:variable") — Brent's root-finding method of function "1:function", giving a couple of delimiters "2:condition" and "3:condition"; calculation have at least "4:condition" function precision or "5:condition" variable precision. A "6:number" different from 0 set your custom max number of iterations, a "7:variable" different from 0 show you the number of iterations, a "8:variable" different from 0 show you a step-by-step summary and a "9:variable" different from 0 save a CSV summary into the current working directory.
Broyden("1:function", "2:condition") — Broyden's root-finding method of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have at least 4 decimal places function(s) precision.
Broyden("1:function", "2:condition", "3:condition") — Broyden's root-finding method of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have at least "3:condition" function(s) precision.
Broyden("1:function", "2:condition", "3:condition", "4:condition") — Broyden's root-finding method of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have at least "3:condition" function(s) precision or "4:condition" variable(s) precision.
Broyden("1:function", "2:condition", "3:condition", "4:condition", "5:number", "6:variable", "7:variable", "8:variable") — Broyden's root-finding method of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have at least "3:condition" function(s) precision or "4:condition" variable(s) precision. A "5:number" different from 0 set your custom max number of iterations, a "6:variable" different from 0 show you the number of iterations, a "7:variable" different from 0 show you a step-by-step summary and a "8:variable" different from 0 save a CSV summary into the current working directory.
FindRoot("1:function", "2:condition") — Find root(s) of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have at least 4 decimal places function(s) precision.
FindRoot("1:function", "2:condition", "3:condition") — Find root(s) of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have at least "3:condition" function(s) precision.
FindRoot("1:function", "2:condition", "3:condition", "4:condition") — Find root(s) of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have at least "3:condition" function(s) precision or "4:condition" variable(s) precision.
GaussNewton.CD("1:function", "2:condition") — Gauss-Newton optimization algorithm of function(s) "1:function" using central differences, giving an initial guess "2:condition" for each variable; calculation have at least 4 decimal places variable(s) precision. Alghorithm use a constant step length.
GaussNewton.CD("1:function", "2:condition", "3:condition") — Gauss-Newton optimization algorithm of function(s) "1:function" using central differences, giving an initial guess "2:condition" for each variable; calculation have "3:condition" variable(s) precision. Alghorithm use a constant step length.
GaussNewton.CD("1:function", "2:condition", "3:condition", "4:condition") — Gauss-Newton optimization algorithm of function(s) "1:function" using central differences, giving an initial guess "2:condition" for each variable; calculation have "3:condition" variable(s) precision. A "4:condition" different from 0 set your custom perturbation. Alghorithm use a constant step length.
GaussNewton.CD("1:function", "2:condition", "3:condition", "4:condition", "5:number", "6:variable", "7:variable", "8:variable") — Gauss-Newton optimization algorithm of function(s) "1:function" using central differences, giving an initial guess "2:condition" for each variable; calculation have "3:condition" variable(s) precision. A "4:condition" different from 0 set your custom perturbation. A "5:number" different from 0 set your custom max number of iterations, a "6:variable" different from 0 show you the number of iterations, a "7:variable" different from 0 show you a step-by-step summary and a "8:variable" different from 0 save a CSV summary into the current working directory. Alghorithm use a constant step length.
GaussNewton.GSS;CD("1:function", "2:condition") — Gauss-Newton optimization algorithm of function(s) "1:function" using central differences, giving an initial guess "2:condition" for each variable; calculation have at least 4 decimal places variable(s) precision. Alghorithm use a step length based on a Golden Section Search line search strategy.
GaussNewton.GSS;CD("1:function", "2:condition", "3:condition") — Gauss-Newton optimization algorithm of function(s) "1:function" using central differences, giving an initial guess "2:condition" for each variable; calculation have "3:condition" variable(s) precision. Alghorithm use a step length based on a Golden Section Search line search strategy.
GaussNewton.GSS;CD("1:function", "2:condition", "3:condition", "4:condition") — Gauss-Newton optimization algorithm of function(s) "1:function" using central differences, giving an initial guess "2:condition" for each variable; calculation have "3:condition" variable(s) precision. A "4:condition" different from 0 set your custom perturbation. Alghorithm use a step length based on a Golden Section Search line search strategy.
GaussNewton.GSS;CD("1:function", "2:condition", "3:condition", "4:condition", "5:number", "6:variable", "7:variable", "8:variable") — Gauss-Newton optimization algorithm of function(s) "1:function" using central differences, giving an initial guess "2:condition" for each variable; calculation have "3:condition" variable(s) precision. A "4:condition" different from 0 set your custom perturbation. A "5:number" different from 0 set your custom max number of iterations, a "6:variable" different from 0 show you the number of iterations, a "7:variable" different from 0 show you a step-by-step summary and a "8:variable" different from 0 save a CSV summary into the current working directory. Alghorithm use a step length based on a Golden Section Search line search strategy.
GaussNewton.GSS("1:function", "2:condition") — Gauss-Newton optimization algorithm of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have at least 4 decimal places variable(s) precision. Alghorithm use a step length based on a Golden Section Search line search strategy.
GaussNewton.GSS("1:function", "2:condition", "3:condition") — Gauss-Newton optimization algorithm of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have "3:condition" variable(s) precision. Alghorithm use a step length based on a Golden Section Search line search strategy.
GaussNewton.GSS("1:function", "2:condition", "3:condition", "4:number", "5:variable", "6:variable", "7:variable") — Gauss-Newton optimization algorithm of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have "3:condition" variable(s) precision. A "4:number" different from 0 set your custom max number of iterations, a "5:variable" different from 0 show you the number of iterations, a "6:variable" different from 0 show you a step-by-step summary and a "7:variable" different from 0 save a CSV summary into the current working directory. Alghorithm use a step length based on a Golden Section Search line search strategy.
GaussNewton("1:function", "2:condition") — Gauss-Newton optimization algorithm of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have at least 4 decimal places variable(s) precision. Alghorithm use a constant step length.
GaussNewton("1:function", "2:condition", "3:condition") — Gauss-Newton optimization algorithm of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have "3:condition" variable(s) precision. Alghorithm use a constant step length.
GaussNewton("1:function", "2:condition", "3:condition", "4:number", "5:variable", "6:variable", "7:variable") — Gauss-Newton optimization algorithm of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have "3:condition" variable(s) precision. A "4:number" different from 0 set your custom max number of iterations, a "5:variable" different from 0 show you the number of iterations, a "6:variable" different from 0 show you a step-by-step summary and a "7:variable" different from 0 save a CSV summary into the current working directory. Alghorithm use a constant step length.
GoldenSectionSearch.max("1:function", "2:condition", "3:condition") — Golden Section Search extremum finding of function "1:function", giving a couple of delimiters "2:condition" and "3:condition"; calculation have at least 4 decimal places variable precision.
GoldenSectionSearch.max("1:function", "2:condition", "3:condition", "4:condition") — Golden Section Search extremum finding of function "1:function", giving a couple of delimiters "2:condition" and "3:condition"; calculation have at least "4:condition" variable precision.
GoldenSectionSearch.max("1:function", "2:condition", "3:condition", "4:condition", "5:number", "6:variable", "7:variable", "8:variable") — Golden Section Search extremum finding of function "1:function", giving a couple of delimiters "2:condition" and "3:condition"; calculation have at least "4:condition" variable precision. A "5:number" different from 0 set your custom max number of iterations, a "6:variable" different from 0 show you the number of iterations, a "7:variable" different from 0 show you a step-by-step summary and a "8:variable" different from 0 save a CSV summary into the current working directory.
GoldenSectionSearch.min("1:function", "2:condition", "3:condition") — Golden Section Search extremum finding of function "1:function", giving a couple of delimiters "2:condition" and "3:condition"; calculation have at least 4 decimal places variable precision.
GoldenSectionSearch.min("1:function", "2:condition", "3:condition", "4:condition") — Golden Section Search extremum finding of function "1:function", giving a couple of delimiters "2:condition" and "3:condition"; calculation have at least "4:condition" variable precision.
GoldenSectionSearch.min("1:function", "2:condition", "3:condition", "4:condition", "5:number", "6:variable", "7:variable", "8:variable") — Golden Section Search extremum finding of function "1:function", giving a couple of delimiters "2:condition" and "3:condition"; calculation have at least "4:condition" variable precision. A "5:number" different from 0 set your custom max number of iterations, a "6:variable" different from 0 show you the number of iterations, a "7:variable" different from 0 show you a step-by-step summary and a "8:variable" different from 0 save a CSV summary into the current working directory.
Gradient.CD("1:function", "2:variable") — Numerical first order central differences of "1:function" evaluated at "2:variable"; returns Gradients or 1st order differentiations.
Gradient.CD("1:function", "2:variable", "3:variable") — Numerical first order central differences of "1:function" evaluated at "2:variable" using a "3:variable" perturbation; returns Gradients or 1st order differentiations.
Gradient("1:function", "2:variable") — First order derivatives of "1:function" evaluated at "2:variable"; returns Gradients or 1st order differentiations.
GradientAscent.GSS("1:function", "2:condition") — Gradient ascent optimization algorithm of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have at least 4 decimal places variable(s) precision. Alghorithm use a step length based on a Golden Section Search line search strategy.
GradientAscent.GSS("1:function", "2:condition", "3:condition") — Gradient ascent optimization algorithm of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have "3:condition" variable(s) precision. Alghorithm use a step length based on a Golden Section Search line search strategy.
GradientAscent.GSS("1:function", "2:condition", "3:condition", "4:number", "5:variable", "6:variable", "7:variable") — Gradient ascent optimization algorithm of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have "3:condition" variable(s) precision. A "4:number" different from 0 set your custom max number of iterations, a "5:variable" different from 0 show you the number of iterations, a "6:variable" different from 0 show you a step-by-step summary and a "7:variable" different from 0 save a CSV summary into the current working directory. Alghorithm use a step length based on a Golden Section Search line search strategy.
GradientAscent("1:function", "2:condition") — Gradient ascent optimization algorithm of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have at least 4 decimal places variable(s) precision. Alghorithm use a constant step length.
GradientAscent("1:function", "2:condition", "3:condition") — Gradient ascent optimization algorithm of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have "3:condition" variable(s) precision. Alghorithm use a constant step length.
GradientAscent("1:function", "2:condition", "3:condition", "4:number", "5:variable", "6:variable", "7:variable") — Gradient ascent optimization algorithm of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have "3:condition" variable(s) precision. A "4:number" different from 0 set your custom max number of iterations, a "5:variable" different from 0 show you the number of iterations, a "6:variable" different from 0 show you a step-by-step summary and a "7:variable" different from 0 save a CSV summary into the current working directory. Alghorithm use a constant step length.
GradientDescent.GSS("1:function", "2:condition") — Gradient descent optimization algorithm of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have at least 4 decimal places variable(s) precision. Alghorithm use a step length based on a Golden Section Search line search strategy.
GradientDescent.GSS("1:function", "2:condition", "3:condition") — Gradient descent optimization algorithm of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have "3:condition" variable(s) precision. Alghorithm use a step length based on a Golden Section Search line search strategy.
GradientDescent.GSS("1:function", "2:condition", "3:condition", "4:number", "5:variable", "6:variable", "7:variable") — Gradient descent optimization algorithm of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have "3:condition" variable(s) precision. A "4:number" different from 0 set your custom max number of iterations, a "5:variable" different from 0 show you the number of iterations, a "6:variable" different from 0 show you a step-by-step summary and a "7:variable" different from 0 save a CSV summary into the current working directory. Alghorithm use a step length based on a Golden Section Search line search strategy.
GradientDescent("1:function", "2:condition") — Gradient descent optimization algorithm of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have at least 4 decimal places variable(s) precision. Alghorithm use a constant step length.
GradientDescent("1:function", "2:condition", "3:condition") — Gradient descent optimization algorithm of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have "3:condition" variable(s) precision. Alghorithm use a constant step length.
GradientDescent("1:function", "2:condition", "3:condition", "4:number", "5:variable", "6:variable", "7:variable") — Gradient descent optimization algorithm of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have "3:condition" variable(s) precision. A "4:number" different from 0 set your custom max number of iterations, a "5:variable" different from 0 show you the number of iterations, a "6:variable" different from 0 show you a step-by-step summary and a "7:variable" different from 0 save a CSV summary into the current working directory. Alghorithm use a constant step length.
Hessian.CD("1:function", "2:variable") — Numerical second order central differences of "1:function" evaluated at "2:variable"; returns Hessians or 2nd order differentiations.
Hessian.CD("1:function", "2:variable", "3:variable") — Numerical second order central differences of "1:function" evaluated at "2:variable" using a "3:variable" perturbation; returns Hessians or 2nd order differentiations.
Hessian("1:function", "2:variable") — Second order derivatives of "1:function" evaluated at "2:variable"; returns Hessians or 2nd order differentiations.
HRE.B("1:function", "2:condition") — Homotopy root-estimation method of function(s) "1:function", giving an initial guess "2:condition" for each variable, using the Broyden's algorithm; calculation have at least 4 decimal places function(s) precision.
HRE.B("1:function", "2:condition", "3:condition") — Homotopy root-estimation method of function(s) "1:function", giving an initial guess "2:condition" for each variable, using the Broyden's algorithm; calculation have at least "3:condition" function(s) precision.
HRE.B("1:function", "2:condition", "3:condition", "4:condition") — Homotopy root-estimation method of function(s) "1:function", giving an initial guess "2:condition" for each variable, using the Broyden's algorithm; calculation have at least "3:condition" function(s) precision or "4:condition" variable(s) precision.
HRE.B("1:function", "2:condition", "3:condition", "4:condition", "5:number", "6:variable", "7:variable", "8:variable") — Homotopy root-estimation method of function(s) "1:function", giving an initial guess "2:condition" for each variable, using the Broyden's algorithm; calculation have at least "3:condition" function(s) precision or "4:condition" variable(s) precision. A "5:number" different from 0 set your custom number of homotopy transformations, a "6:variable" different from 0 show you the number of iterations, a "7:variable" different from 0 show you a step-by-step summary and a "8:variable" different from 0 save a CSV summary into the current working directory.
HRE.NR;CD("1:function", "2:condition") — Homotopy root-estimation method of function(s) "1:function", giving an initial guess "2:condition" for each variable, using the central differences Newton's algorithm; calculation have at least 4 decimal places function(s) precision.
HRE.NR;CD("1:function", "2:condition", "3:condition") — Homotopy root-estimation method of function(s) "1:function", giving an initial guess "2:condition" for each variable, using the central differences Newton's algorithm; calculation have at least "3:condition" function(s) precision.
HRE.NR;CD("1:function", "2:condition", "3:condition", "4:condition") — Homotopy root-estimation method of function(s) "1:function", giving an initial guess "2:condition" for each variable, using the central differences Newton's algorithm; calculation have at least "3:condition" function(s) precision or "4:condition" variable(s) precision.
HRE.NR;CD("1:function", "2:condition", "3:condition", "4:condition", "5:condition") — Homotopy root-estimation method of function(s) "1:function", giving an initial guess "2:condition" for each variable, using the central differences Newton's algorithm; calculation have at least "3:condition" function(s) precision or "4:condition" variable(s) precision. A "5:condition" different from 0 set your custom perturbation.
HRE.NR;CD("1:function", "2:condition", "3:condition", "4:condition", "5:condition", "6:number", "7:variable", "8:variable", "9:variable") — Homotopy root-estimation method of function(s) "1:function", giving an initial guess "2:condition" for each variable, using the central differences Newton's algorithm; calculation have at least "3:condition" function(s) precision or "4:condition" variable(s) precision. A "5:condition" different from 0 set your custom perturbation. A "6:number" different from 0 set your custom number of homotopy transformations, a "7:variable" different from 0 show you the number of iterations, a "8:variable" different from 0 show you a step-by-step summary and a "9:variable" different from 0 save a CSV summary into the current working directory.
HRE.NR("1:function", "2:condition") — Homotopy root-estimation method of function(s) "1:function", giving an initial guess "2:condition" for each variable, using the Newton's algorithm; calculation have at least 4 decimal places function(s) precision.
HRE.NR("1:function", "2:condition", "3:condition") — Homotopy root-estimation method of function(s) "1:function", giving an initial guess "2:condition" for each variable, using the Newton's algorithm; calculation have at least "3:condition" function(s) precision.
HRE.NR("1:function", "2:condition", "3:condition", "4:condition") — Homotopy root-estimation method of function(s) "1:function", giving an initial guess "2:condition" for each variable, using the Newton's algorithm; calculation have at least "3:condition" function(s) precision or "4:condition" variable(s) precision.
HRE.NR("1:function", "2:condition", "3:condition", "4:condition", "5:number", "6:variable", "7:variable", "8:variable") — Homotopy root-estimation method of function(s) "1:function", giving an initial guess "2:condition" for each variable, using the Newton's algorithm; calculation have at least "3:condition" function(s) precision or "4:condition" variable(s) precision. A "5:number" different from 0 set your custom number of homotopy transformations, a "6:variable" different from 0 show you the number of iterations, a "7:variable" different from 0 show you a step-by-step summary and a "8:variable" different from 0 save a CSV summary into the current working directory.
HRE.RK;CD("1:function", "2:condition") — Homotopy root-estimation method of function(s) "1:function", giving an initial guess "2:condition" for each variable, using the Runge-Kutta 4th order central differences algorithm; calculation have at least 4 decimal places function(s) precision.
HRE.RK;CD("1:function", "2:condition", "3:condition") — Homotopy root-estimation method of function(s) "1:function", giving an initial guess "2:condition" for each variable, using the Runge-Kutta 4th order central differences algorithm; calculation have at least "3:condition" function(s) precision.
HRE.RK;CD("1:function", "2:condition", "3:condition", "4:condition") — Homotopy root-estimation method of function(s) "1:function", giving an initial guess "2:condition" for each variable, using the Runge-Kutta 4th order central differences algorithm; calculation have at least "3:condition" function(s) precision or "4:condition" variable(s) precision.
HRE.RK;CD("1:function", "2:condition", "3:condition", "4:condition", "5:condition") — Homotopy root-estimation method of function(s) "1:function", giving an initial guess "2:condition" for each variable, using the Runge-Kutta 4th order central differences algorithm; calculation have at least "3:condition" function(s) precision or "4:condition" variable(s) precision. A "5:condition" different from 0 set your custom perturbation.
HRE.RK;CD("1:function", "2:condition", "3:condition", "4:condition", "5:condition", "6:number", "7:variable", "8:variable", "9:variable") — Homotopy root-estimation method of function(s) "1:function", giving an initial guess "2:condition" for each variable, using the Runge-Kutta 4th order central differences algorithm; calculation have at least "3:condition" function(s) precision or "4:condition" variable(s) precision. A "5:condition" different from 0 set your custom perturbation. A "6:number" different from 0 set your custom number of homotopy transformations, a "7:variable" different from 0 show you the number of iterations, a "8:variable" different from 0 show you a step-by-step summary and a "9:variable" different from 0 save a CSV summary into the current working directory.
HRE.RK("1:function", "2:condition") — Homotopy root-estimation method of function(s) "1:function", giving an initial guess "2:condition" for each variable, using the Runge-Kutta 4th order algorithm; calculation have at least 4 decimal places function(s) precision.
HRE.RK("1:function", "2:condition", "3:condition") — Homotopy root-estimation method of function(s) "1:function", giving an initial guess "2:condition" for each variable, using the Runge-Kutta 4th order algorithm; calculation have at least "3:condition" function(s) precision.
HRE.RK("1:function", "2:condition", "3:condition", "4:condition") — Homotopy root-estimation method of function(s) "1:function", giving an initial guess "2:condition" for each variable, using the Runge-Kutta 4th order algorithm; calculation have at least "3:condition" function(s) precision or "4:condition" variable(s) precision.
HRE.RK("1:function", "2:condition", "3:condition", "4:condition", "5:number", "6:variable", "7:variable", "8:variable") — Homotopy root-estimation method of function(s) "1:function", giving an initial guess "2:condition" for each variable, using the Runge-Kutta 4th order algorithm; calculation have at least "3:condition" function(s) precision or "4:condition" variable(s) precision. A "5:number" different from 0 set your custom number of homotopy transformations, a "6:variable" different from 0 show you the number of iterations, a "7:variable" different from 0 show you a step-by-step summary and a "8:variable" different from 0 save a CSV summary into the current working directory.
Jacobian.CD("1:function", "2:variable") — Numerical first order central differences of "1:function" evaluated at "2:variable"; returns Jacobians or 1st order differentiations.
Jacobian.CD("1:function", "2:variable", "3:variable") — Numerical first order central differences of "1:function" evaluated at "2:variable" using a "3:variable" perturbation; returns Jacobians or 1st order differentiations.
Jacobian("1:function", "2:variable") — First order derivatives of "1:function" evaluated at "2:variable"; returns Jacobians or 1st order differentiations.
LevenbergMarquardt.CD("1:function", "2:condition") — Levenberg-Marquardt optimization algorithm of function(s) "1:function" using central differences, giving an initial guess "2:condition" for each variable; calculation have at least 4 decimal places variable(s) precision. Alghorithm use a constant step length.
LevenbergMarquardt.CD("1:function", "2:condition", "3:condition") — Levenberg-Marquardt optimization algorithm of function(s) "1:function" using central differences, giving an initial guess "2:condition" for each variable; calculation have "3:condition" variable(s) precision. Alghorithm use a constant step length.
LevenbergMarquardt.CD("1:function", "2:condition", "3:condition", "4:condition") — Levenberg-Marquardt optimization algorithm of function(s) "1:function" using central differences, giving an initial guess "2:condition" for each variable; calculation have "3:condition" variable(s) precision. A "4:condition" different from 0 set your custom perturbation. Alghorithm use a constant step length.
LevenbergMarquardt.CD("1:function", "2:condition", "3:condition", "4:condition", "5:number", "6:variable", "7:variable", "8:variable") — Levenberg-Marquardt optimization algorithm of function(s) "1:function" using central differences, giving an initial guess "2:condition" for each variable; calculation have "3:condition" variable(s) precision. A "4:condition" different from 0 set your custom perturbation. A "5:number" different from 0 set your custom max number of iterations, a "6:variable" different from 0 show you the number of iterations, a "7:variable" different from 0 show you a step-by-step summary and a "8:variable" different from 0 save a CSV summary into the current working directory. Alghorithm use a constant step length.
LevenbergMarquardt("1:function", "2:condition") — Levenberg-Marquardt optimization algorithm of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have at least 4 decimal places variable(s) precision. Alghorithm use a constant step length.
LevenbergMarquardt("1:function", "2:condition", "3:condition") — Levenberg-Marquardt optimization algorithm of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have "3:condition" variable(s) precision. Alghorithm use a constant step length.
LevenbergMarquardt("1:function", "2:condition", "3:condition", "4:number", "5:variable", "6:variable", "7:variable") — Levenberg-Marquardt optimization algorithm of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have "3:condition" variable(s) precision. A "4:number" different from 0 set your custom max number of iterations, a "5:variable" different from 0 show you the number of iterations, a "6:variable" different from 0 show you a step-by-step summary and a "7:variable" different from 0 save a CSV summary into the current working directory. Alghorithm use a constant step length.
mapUnknowns("1:function", "2:condition") — Symbolical variables' mapping; returns a vector of unassigned variables/elements contained in "1:function", according with the "2:condition" pattern.
mapUnknowns("1:function", "2:condition", "3:name") — Symbolical variables' mapping; returns a vector of unassigned elements contained in "1:function", according with the "2:condition" pattern, using "3:name" as unknown name.
NCGM.CD("1:function", "2:condition") — Nonlinear Conjugate Gradient Method optimization algorithm of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have at least 4 decimal places variable(s) precision. Alghorithm use a step length based on a Golden Section Search line search strategy.
NCGM.CD("1:function", "2:condition", "3:condition") — Nonlinear Conjugate Gradient Method optimization algorithm of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have "3:condition" variable(s) precision. Alghorithm use a step length based on a Golden Section Search line search strategy.
NCGM.CD("1:function", "2:condition", "3:condition", "4:condition") — Nonlinear Conjugate Gradient Method optimization algorithm of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have "3:condition" variable(s) precision. A "4:condition" different from 0 set your custom perturbation. Alghorithm use a step length based on a Golden Section Search line search strategy.
NCGM.CD("1:function", "2:condition", "3:condition", "4:condition", "5:number", "6:variable", "7:variable", "8:variable") — Nonlinear Conjugate Gradient Method optimization algorithm of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have "3:condition" variable(s) precision. A "4:condition" different from 0 set your custom perturbation. A "5:number" different from 0 set your custom max number of iterations, a "6:variable" different from 0 show you the number of iterations, a "7:variable" different from 0 show you a step-by-step summary and a "8:variable" different from 0 save a CSV summary into the current working directory. Alghorithm use a step length based on a Golden Section Search line search strategy.
NCGM("1:function", "2:condition") — Nonlinear Conjugate Gradient Method optimization algorithm of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have at least 4 decimal places variable(s) precision. Alghorithm use a step length based on a Golden Section Search line search strategy.
NCGM("1:function", "2:condition", "3:condition") — Nonlinear Conjugate Gradient Method optimization algorithm of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have "3:condition" variable(s) precision. Alghorithm use a step length based on a Golden Section Search line search strategy.
NCGM("1:function", "2:condition", "3:condition", "4:number", "5:variable", "6:variable", "7:variable") — Nonlinear Conjugate Gradient Method optimization algorithm of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have "3:condition" variable(s) precision. A "4:number" different from 0 set your custom max number of iterations, a "5:variable" different from 0 show you the number of iterations, a "6:variable" different from 0 show you a step-by-step summary and a "7:variable" different from 0 save a CSV summary into the current working directory. Alghorithm use a step length based on a Golden Section Search line search strategy.
NelderMead("1:function", "2:condition", "3:condition", "4:condition", "5:condition", "6:condition", "7:number", "8:variable", "9:variable", "10:variable") — Nelder-Mead optimization algorithm of function(s) "1:function", giving an initial simplex or an initial guess "2:condition"; calculation have "3:condition" standard deviation precision for function(s) on the simplex. A "4:number" different from 0 set your custom reflection coefficient, a "5:number" different from 0 set your custom contraction coefficient and a "6:number" different from 0 set your custom expansion coefficient. A "7:number" different from 0 set your custom max number of iterations, a "8:variable" different from 0 show you the number of iterations, a "9:variable" different from 0 show you a step-by-step summary and a "10:variable" different from 0 save a CSV summary into the current working directory.
NelderMead("1:function", "2:condition") — Nelder-Mead optimization algorithm of function(s) "1:function", giving an initial simplex or an initial guess "2:condition"; calculation have at least 4 decimal places standard deviation precision for function(s) on the simplex.
NelderMead("1:function", "2:condition", "3:condition") — Nelder-Mead optimization algorithm of function(s) "1:function", giving an initial simplex or an initial guess "2:condition"; calculation have "3:condition" standard deviation precision for function(s) on the simplex.
NelderMead("1:function", "2:condition", "3:condition", "4:condition", "5:condition", "6:condition") — Nelder-Mead optimization algorithm of function(s) "1:function", giving an initial simplex or an initial guess "2:condition"; calculation have "3:condition" standard deviation precision for function(s) on the simplex. A "4:number" different from 0 set your custom reflection coefficient, a "5:number" different from 0 set your custom contraction coefficient and a "6:number" different from 0 set your custom expansion coefficient.
NewtonMethod.CD("1:function", "2:condition") — Newton's optimization algorithm of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have at least 4 decimal places variable(s) precision.
NewtonMethod.CD("1:function", "2:condition", "3:condition") — Newton's optimization algorithm of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have "3:condition" variable(s) precision.
NewtonMethod.CD("1:function", "2:condition", "3:condition", "4:condition") — Newton's optimization algorithm of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have "3:condition" variable(s) precision. A "4:condition" different from 0 set your custom perturbation.
NewtonMethod.CD("1:function", "2:condition", "3:condition", "4:condition", "5:number", "6:variable", "7:variable", "8:variable") — Newton's optimization algorithm of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have "3:condition" variable(s) precision. A "4:condition" different from 0 set your custom perturbation. A "5:number" different from 0 set your custom max number of iterations, a "6:variable" different from 0 show you the number of iterations, a "7:variable" different from 0 show you a step-by-step summary and a "8:variable" different from 0 save a CSV summary into the current working directory.
NewtonMethod.GSS;CD("1:function", "2:condition") — Newton's optimization algorithm of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have at least 4 decimal places variable(s) precision. Alghorithm use a step length based on a Golden Section Search line search strategy.
NewtonMethod.GSS;CD("1:function", "2:condition", "3:condition") — Newton's optimization algorithm of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have "3:condition" variable(s) precision. Alghorithm use a step length based on a Golden Section Search line search strategy.
NewtonMethod.GSS;CD("1:function", "2:condition", "3:condition", "4:condition") — Newton's optimization algorithm of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have "3:condition" variable(s) precision. A "4:condition" different from 0 set your custom perturbation. Alghorithm use a step length based on a Golden Section Search line search strategy.
NewtonMethod.GSS;CD("1:function", "2:condition", "3:condition", "4:condition", "5:number", "6:variable", "7:variable", "8:variable") — Newton's optimization algorithm of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have "3:condition" variable(s) precision. A "4:condition" different from 0 set your custom perturbation. A "5:number" different from 0 set your custom max number of iterations, a "6:variable" different from 0 show you the number of iterations, a "7:variable" different from 0 show you a step-by-step summary and a "8:variable" different from 0 save a CSV summary into the current working directory. Alghorithm use a step length based on a Golden Section Search line search strategy.
NewtonMethod.GSS("1:function", "2:condition") — Newton's optimization algorithm of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have at least 4 decimal places variable(s) precision. Alghorithm use a step length based on a Golden Section Search line search strategy.
NewtonMethod.GSS("1:function", "2:condition", "3:condition") — Newton's optimization algorithm of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have "3:condition" variable(s) precision. Alghorithm use a step length based on a Golden Section Search line search strategy.
NewtonMethod.GSS("1:function", "2:condition", "3:condition", "4:number", "5:variable", "6:variable", "7:variable") — Newton's optimization algorithm of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have "3:condition" variable(s) precision. A "4:number" different from 0 set your custom max number of iterations, a "5:variable" different from 0 show you the number of iterations, a "6:variable" different from 0 show you a step-by-step summary and a "7:variable" different from 0 save a CSV summary into the current working directory. Alghorithm use a step length based on a Golden Section Search line search strategy.
NewtonMethod("1:function", "2:condition") — Newton's optimization algorithm of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have at least 4 decimal places variable(s) precision.
NewtonMethod("1:function", "2:condition", "3:condition") — Newton's optimization algorithm of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have "3:condition" variable(s) precision.
NewtonMethod("1:function", "2:condition", "3:condition", "4:number", "5:variable", "6:variable", "7:variable") — Newton's optimization algorithm of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have "3:condition" variable(s) precision. A "4:number" different from 0 set your custom max number of iterations, a "5:variable" different from 0 show you the number of iterations, a "6:variable" different from 0 show you a step-by-step summary and a "7:variable" different from 0 save a CSV summary into the current working directory.
NewtonRaphson.CD("1:function", "2:condition") — Newton's root-finding method of function(s) "1:function" using central differences, giving an initial guess "2:condition" for each variable; calculation have at least 4 decimal places function(s) precision.
NewtonRaphson.CD("1:function", "2:condition", "3:condition") — Newton's root-finding method of function(s) "1:function" using central differences, giving an initial guess "2:condition" for each variable; calculation have at least "3:condition" function(s) precision.
NewtonRaphson.CD("1:function", "2:condition", "3:condition", "4:condition") — Newton's root-finding method of function(s) "1:function" using central differences, giving an initial guess "2:condition" for each variable; calculation have at least "3:condition" function(s) precision or "4:condition" variable(s) precision.
NewtonRaphson.CD("1:function", "2:condition", "3:condition", "4:condition", "5:condition") — Newton's root-finding method of function(s) "1:function" using central differences, giving an initial guess "2:condition" for each variable; calculation have at least "3:condition" function(s) precision or "4:condition" variable(s) precision. A "5:condition" different from 0 set your custom perturbation.
NewtonRaphson.CD("1:function", "2:condition", "3:condition", "4:condition", "5:condition", "6:number", "7:variable", "8:variable", "9:variable") — Newton's root-finding method of function(s) "1:function" using central differences, giving an initial guess "2:condition" for each variable; calculation have at least "3:condition" function(s) precision or "4:condition" variable(s) precision. A "5:condition" different from 0 set your custom perturbation. A "6:number" different from 0 set your custom max number of iterations, a "7:variable" different from 0 show you the number of iterations, a "8:variable" different from 0 show you a step-by-step summary and a "9:variable" different from 0 save a CSV summary into the current working directory.
NewtonRaphson("1:function", "2:condition") — Newton's root-finding method of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have at least 4 decimal places function(s) precision.
NewtonRaphson("1:function", "2:condition", "3:condition") — Newton's root-finding method of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have at least "3:condition" function(s) precision.
NewtonRaphson("1:function", "2:condition", "3:condition", "4:condition") — Newton's root-finding method of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have at least "3:condition" function(s) precision or "4:condition" variable(s) precision.
NewtonRaphson("1:function", "2:condition", "3:condition", "4:condition", "5:number", "6:variable", "7:variable", "8:variable") — Newton's root-finding method of function(s) "1:function", giving an initial guess "2:condition" for each variable; calculation have at least "3:condition" function(s) precision or "4:condition" variable(s) precision. A "5:number" different from 0 set your custom max number of iterations, a "6:variable" different from 0 show you the number of iterations, a "7:variable" different from 0 show you a step-by-step summary and a "8:variable" different from 0 save a CSV summary into the current working directory.
Ridder("1:function", "2:condition", "3:condition") — Brent's root-finding method of function "1:function", giving a couple of delimiters "2:condition" and "3:condition"; calculation have at least 4 decimal places function precision.
Ridder("1:function", "2:condition", "3:condition", "4:condition") — Brent's root-finding method of function "1:function", giving a couple of delimiters "2:condition" and "3:condition"; calculation have at least "4:condition" function precision.
Ridder("1:function", "2:condition", "3:condition", "4:condition", "5:condition") — Brent's root-finding method of function "1:function", giving a couple of delimiters "2:condition" and "3:condition"; calculation have at least "4:condition" function precision or "5:condition" variable precision.
Ridder("1:function", "2:condition", "3:condition", "4:condition", "5:condition", "6:number", "7:variable", "8:variable", "9:variable") — Brent's root-finding method of function "1:function", giving a couple of delimiters "2:condition" and "3:condition"; calculation have at least "4:condition" function precision or "5:condition" variable precision. A "6:number" different from 0 set your custom max number of iterations, a "7:variable" different from 0 show you the number of iterations, a "8:variable" different from 0 show you a step-by-step summary and a "9:variable" different from 0 save a CSV summary into the current working directory.
Secant("1:function", "2:condition", "3:condition") — Secant root-finding method of function "1:function", giving a couple of delimiters "2:condition" and "3:condition"; calculation have at least 4 decimal places function precision.
Secant("1:function", "2:condition", "3:condition", "4:condition") — Secant root-finding method of function "1:function", giving a couple of delimiters "2:condition" and "3:condition"; calculation have at least "4:condition" function precision.
Secant("1:function", "2:condition", "3:condition", "4:condition", "5:condition") — Secant root-finding method of function "1:function", giving a couple of delimiters "2:condition" and "3:condition"; calculation have at least "4:condition" function precision or "5:condition" variable precision.
Secant("1:function", "2:condition", "3:condition", "4:condition", "5:condition", "6:number", "7:variable", "8:variable", "9:variable") — Secant root-finding method of function "1:function", giving a couple of initial guess "2:condition" and "3:condition"; calculation have at least "4:condition" function precision or "5:condition" variable precision. A "6:number" different from 0 set your custom max number of iterations, a "7:variable" different from 0 show you the number of iterations, a "8:variable" different from 0 show you a step-by-step summary and a "9:variable" different from 0 save a CSV summary into the current working directory.
Taylor("1:function", "2:variable", "3:number") — Taylor series expansion of "1:function" about the "2:variable" point up to the "3:number"th order.
Unknowns("variable") — Variables' detection; returns a vector of unassigned variables contained in "1:variable".