Функции (135 шт.):
- BDQRF("1:функция", "2:условие", "3:условие") — 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:функция", "2:условие", "3:условие", "4:условие") — 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:функция", "2:условие", "3:условие", "4:условие", "5:условие") — 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:функция", "2:условие", "3:условие", "4:условие", "5:условие", "6:число", "7:переменная", "8:переменная", "9:переменная") — 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:функция", "2:условие", "3:условие") — 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:функция", "2:условие", "3:условие", "4:условие") — 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:функция", "2:условие", "3:условие", "4:условие", "5:условие") — 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:функция", "2:условие", "3:условие", "4:условие", "5:условие", "6:переменная", "7:переменная", "8:переменная") — 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:функция", "2:условие", "3:условие") — 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:функция", "2:условие", "3:условие", "4:условие") — 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:функция", "2:условие", "3:условие", "4:условие", "5:условие") — 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:функция", "2:условие", "3:условие", "4:условие", "5:условие", "6:число", "7:переменная", "8:переменная", "9:переменная") — 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:функция", "2:условие") — 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:функция", "2:условие", "3:условие") — 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:функция", "2:условие", "3:условие", "4:условие") — 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:функция", "2:условие", "3:условие", "4:условие", "5:число", "6:переменная", "7:переменная", "8:переменная") — 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:функция", "2:условие") — 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:функция", "2:условие", "3:условие") — 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:функция", "2:условие", "3:условие", "4:условие") — 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:функция", "2:условие") — 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:функция", "2:условие", "3:условие") — 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:функция", "2:условие", "3:условие", "4:условие") — 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:функция", "2:условие", "3:условие", "4:условие", "5:число", "6:переменная", "7:переменная", "8:переменная") — 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:функция", "2:условие") — 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:функция", "2:условие", "3:условие") — 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:функция", "2:условие", "3:условие", "4:условие") — 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:функция", "2:условие", "3:условие", "4:условие", "5:число", "6:переменная", "7:переменная", "8:переменная") — 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:функция", "2:условие") — 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:функция", "2:условие", "3:условие") — 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:функция", "2:условие", "3:условие", "4:число", "5:переменная", "6:переменная", "7:переменная") — 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:функция", "2:условие") — 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:функция", "2:условие", "3:условие") — 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:функция", "2:условие", "3:условие", "4:число", "5:переменная", "6:переменная", "7:переменная") — 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:функция", "2:условие", "3:условие") — 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:функция", "2:условие", "3:условие", "4:условие") — 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:функция", "2:условие", "3:условие", "4:условие", "5:число", "6:переменная", "7:переменная", "8:переменная") — 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:функция", "2:условие", "3:условие") — 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:функция", "2:условие", "3:условие", "4:условие") — 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:функция", "2:условие", "3:условие", "4:условие", "5:число", "6:переменная", "7:переменная", "8:переменная") — 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:функция", "2:переменная") — Numerical first order central differences of "1:function" evaluated at "2:variable"; returns Gradients or 1st order differentiations.
- Gradient.CD("1:функция", "2:переменная", "3:переменная") — 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:функция", "2:переменная") — First order derivatives of "1:function" evaluated at "2:variable"; returns Gradients or 1st order differentiations.
- GradientAscent.GSS("1:функция", "2:условие") — 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:функция", "2:условие", "3:условие") — 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:функция", "2:условие", "3:условие", "4:число", "5:переменная", "6:переменная", "7:переменная") — 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:функция", "2:условие") — 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:функция", "2:условие", "3:условие") — 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:функция", "2:условие", "3:условие", "4:число", "5:переменная", "6:переменная", "7:переменная") — 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:функция", "2:условие") — 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:функция", "2:условие", "3:условие") — 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:функция", "2:условие", "3:условие", "4:число", "5:переменная", "6:переменная", "7:переменная") — 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:функция", "2:условие") — 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:функция", "2:условие", "3:условие") — 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:функция", "2:условие", "3:условие", "4:число", "5:переменная", "6:переменная", "7:переменная") — 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:функция", "2:переменная") — Numerical second order central differences of "1:function" evaluated at "2:variable"; returns Hessians or 2nd order differentiations.
- Hessian.CD("1:функция", "2:переменная", "3:переменная") — 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:функция", "2:переменная") — Second order derivatives of "1:function" evaluated at "2:variable"; returns Hessians or 2nd order differentiations.
- HRE.B("1:функция", "2:условие") — 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:функция", "2:условие", "3:условие") — 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:функция", "2:условие", "3:условие", "4:условие") — 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:функция", "2:условие", "3:условие", "4:условие", "5:число", "6:переменная", "7:переменная", "8:переменная") — 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:функция", "2:условие") — 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:функция", "2:условие", "3:условие") — 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:функция", "2:условие", "3:условие", "4:условие") — 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:функция", "2:условие", "3:условие", "4:условие", "5:условие") — 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:функция", "2:условие", "3:условие", "4:условие", "5:условие", "6:число", "7:переменная", "8:переменная", "9:переменная") — 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:функция", "2:условие") — 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:функция", "2:условие", "3:условие") — 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:функция", "2:условие", "3:условие", "4:условие") — 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:функция", "2:условие", "3:условие", "4:условие", "5:число", "6:переменная", "7:переменная", "8:переменная") — 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:функция", "2:условие") — 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:функция", "2:условие", "3:условие") — 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:функция", "2:условие", "3:условие", "4:условие") — 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:функция", "2:условие", "3:условие", "4:условие", "5:условие") — 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:функция", "2:условие", "3:условие", "4:условие", "5:условие", "6:число", "7:переменная", "8:переменная", "9:переменная") — 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:функция", "2:условие") — 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:функция", "2:условие", "3:условие") — 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:функция", "2:условие", "3:условие", "4:условие") — 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:функция", "2:условие", "3:условие", "4:условие", "5:число", "6:переменная", "7:переменная", "8:переменная") — 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:функция", "2:переменная") — Numerical first order central differences of "1:function" evaluated at "2:variable"; returns Jacobians or 1st order differentiations.
- Jacobian.CD("1:функция", "2:переменная", "3:переменная") — 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:функция", "2:переменная") — First order derivatives of "1:function" evaluated at "2:variable"; returns Jacobians or 1st order differentiations.
- LevenbergMarquardt.CD("1:функция", "2:условие") — 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:функция", "2:условие", "3:условие") — 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:функция", "2:условие", "3:условие", "4:условие") — 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:функция", "2:условие", "3:условие", "4:условие", "5:число", "6:переменная", "7:переменная", "8:переменная") — 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:функция", "2:условие") — 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:функция", "2:условие", "3:условие") — 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:функция", "2:условие", "3:условие", "4:число", "5:переменная", "6:переменная", "7:переменная") — 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:функция", "2:условие") — Symbolical variables' mapping; returns a vector of unassigned variables/elements contained in "1:function", according with the "2:condition" pattern.
- mapUnknowns("1:функция", "2:условие", "3:имя") — 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:функция", "2:условие") — 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:функция", "2:условие", "3:условие") — 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:функция", "2:условие", "3:условие", "4:условие") — 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:функция", "2:условие", "3:условие", "4:условие", "5:число", "6:переменная", "7:переменная", "8:переменная") — 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:функция", "2:условие") — 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:функция", "2:условие", "3:условие") — 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:функция", "2:условие", "3:условие", "4:число", "5:переменная", "6:переменная", "7:переменная") — 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:функция", "2:условие", "3:условие", "4:условие", "5:условие", "6:условие", "7:число", "8:переменная", "9:переменная", "10:переменная") — 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:функция", "2:условие") — 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:функция", "2:условие", "3:условие") — 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:функция", "2:условие", "3:условие", "4:условие", "5:условие", "6:условие") — 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:функция", "2:условие") — 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:функция", "2:условие", "3:условие") — 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:функция", "2:условие", "3:условие", "4:условие") — 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:функция", "2:условие", "3:условие", "4:условие", "5:число", "6:переменная", "7:переменная", "8:переменная") — 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:функция", "2:условие") — 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:функция", "2:условие", "3:условие") — 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:функция", "2:условие", "3:условие", "4:условие") — 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:функция", "2:условие", "3:условие", "4:условие", "5:число", "6:переменная", "7:переменная", "8:переменная") — 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:функция", "2:условие") — 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:функция", "2:условие", "3:условие") — 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:функция", "2:условие", "3:условие", "4:число", "5:переменная", "6:переменная", "7:переменная") — 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:функция", "2:условие") — 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:функция", "2:условие", "3:условие") — 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:функция", "2:условие", "3:условие", "4:число", "5:переменная", "6:переменная", "7:переменная") — 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:функция", "2:условие") — 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:функция", "2:условие", "3:условие") — 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:функция", "2:условие", "3:условие", "4:условие") — 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:функция", "2:условие", "3:условие", "4:условие", "5:условие") — 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:функция", "2:условие", "3:условие", "4:условие", "5:условие", "6:число", "7:переменная", "8:переменная", "9:переменная") — 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:функция", "2:условие") — 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:функция", "2:условие", "3:условие") — 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:функция", "2:условие", "3:условие", "4:условие") — 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:функция", "2:условие", "3:условие", "4:условие", "5:число", "6:переменная", "7:переменная", "8:переменная") — 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:функция", "2:условие", "3:условие") — 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:функция", "2:условие", "3:условие", "4:условие") — 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:функция", "2:условие", "3:условие", "4:условие", "5:условие") — 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:функция", "2:условие", "3:условие", "4:условие", "5:условие", "6:число", "7:переменная", "8:переменная", "9:переменная") — 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:функция", "2:условие", "3:условие") — 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:функция", "2:условие", "3:условие", "4:условие") — 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:функция", "2:условие", "3:условие", "4:условие", "5:условие") — 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:функция", "2:условие", "3:условие", "4:условие", "5:условие", "6:число", "7:переменная", "8:переменная", "9:переменная") — 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:функция", "2:переменная", "3:число") — Taylor series expansion of "1:function" about the "2:variable" point up to the "3:number"th order.
- Unknowns("переменная") — Variables' detection; returns a vector of unassigned variables contained in "1:variable".