由Davide Carpi (davide.carpi@gmail.com)在SMath项目的作用域中创建。由smath发布。
这是一个开源项目。MIT许可证下共享的源代码SVN存储库

Features of Nonlinear Solvers

版本1.1.7097.23301

Functions

Additional components that add new mathematical functions to the SMath Studio program, necessary for solving problems from various fields.

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. 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.
  15. 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.
  16. 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.
  17. 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.
  18. 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.
  19. 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.
  20. 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.
  21. 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.
  22. 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.
  23. 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.
  24. 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.
  25. 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.
  26. 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.
  27. 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.
  28. 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.
  29. 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.
  30. 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.
  31. 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.
  32. 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.
  33. 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.
  34. 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.
  35. 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.
  36. 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.
  37. 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.
  38. 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.
  39. 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.
  40. Gradient.CD("1:function", "2:variable")
    Numerical first order central differences of "1:function" evaluated at "2:variable"; returns Gradients or 1st order differentiations.
  41. 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.
  42. Gradient("1:function", "2:variable")
    First order derivatives of "1:function" evaluated at "2:variable"; returns Gradients or 1st order differentiations.
  43. 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.
  44. 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.
  45. 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.
  46. 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.
  47. 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.
  48. 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.
  49. 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.
  50. 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.
  51. 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.
  52. 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.
  53. 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.
  54. 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.
  55. Hessian.CD("1:function", "2:variable")
    Numerical second order central differences of "1:function" evaluated at "2:variable"; returns Hessians or 2nd order differentiations.
  56. 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.
  57. Hessian("1:function", "2:variable")
    Second order derivatives of "1:function" evaluated at "2:variable"; returns Hessians or 2nd order differentiations.
  58. 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.
  59. 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.
  60. 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.
  61. 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.
  62. 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.
  63. 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.
  64. 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.
  65. 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.
  66. 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.
  67. 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.
  68. 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.
  69. 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.
  70. 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.
  71. 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.
  72. 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.
  73. 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.
  74. 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.
  75. 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.
  76. 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.
  77. 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.
  78. 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.
  79. 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.
  80. Jacobian.CD("1:function", "2:variable")
    Numerical first order central differences of "1:function" evaluated at "2:variable"; returns Jacobians or 1st order differentiations.
  81. 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.
  82. Jacobian("1:function", "2:variable")
    First order derivatives of "1:function" evaluated at "2:variable"; returns Jacobians or 1st order differentiations.
  83. 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.
  84. 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.
  85. 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.
  86. 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.
  87. 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.
  88. 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.
  89. 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.
  90. 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.
  91. 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.
  92. 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.
  93. 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.
  94. 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.
  95. 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.
  96. 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.
  97. 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.
  98. 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.
  99. 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.
  100. 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.
  101. 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.
  102. 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.
  103. 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.
  104. 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.
  105. 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.
  106. 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.
  107. 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.
  108. 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.
  109. 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.
  110. 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.
  111. 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.
  112. 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.
  113. 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.
  114. 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.
  115. 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.
  116. 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.
  117. 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.
  118. 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.
  119. 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.
  120. 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.
  121. 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.
  122. 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.
  123. 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.
  124. 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.
  125. 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.
  126. 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.
  127. 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.
  128. 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.
  129. 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.
  130. 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.
  131. 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.
  132. 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.
  133. 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.
  134. 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.
  135. Unknowns("variable")
    Variables' detection; returns a vector of unassigned variables contained in "1:variable".