What'sBest! is an add-in to Excel that allows you to build large scale optimization models in a free form layout within a spreadsheet. What'sBest! combines the proven power of linear, nonlinear and integer optimization with Microsoft Excel -- the most popular and flexible business modeling environment in use today.
What'sBest! 10.0 New Features
New Stochastic Programming Option
- Allows modelling and optimization for models with uncertain elements via multistage stochastic linear, nonlinear and integer stochastic programming (SP)
- Benders decomposition is used for solving large linear SP models.
- Deterministic equivalent method is used for solving nonlinear and integer SP models.
- Support is available for over 20 distribution types (discrete or continuous).
- Sample directly from various statistical distributions
- Variance reduction with Latin-Hyper-Cube and Anti-thetic variates sampling.
- Generation of correlated samples via Pearson, Spearman, or Kendall correlation measures
- Pseudo random uniform generation via a choice of three different generators.
- Large linear models solve an average of 20% faster with improved Primal and Dual Simplex solvers.
- The Integer Solver is an average of 50% faster on a broad range of integer models.
- Substantial improvements have been made to all heuristics for finding close to optimal solutions quickly
- There have also been significant improvements in cut generation for certain types of special model structures.
- Find the best solution to those challenging, nonconvex, nonlinear problems such as in engineering design.
- What’sBest! models can be laid out over a larger area to take advantage of the increased rows and columns in Excel 2007. The maximum number of rows and columns are 16384 columns and 1048576 rows.
- A new feature allows What’s Best! to return a list of the best solutions to integer models. The user can scroll through the solutions, examine them, and select the one of most interest based on the value of a tradeoff variable.
- Easily specify piecewise linear functions such as in quantity discount purchasing. Semi-continuous variables can take a value of 0 or lie within some non-negative range. Special Ordered Sets (SOS) are sets of binary variables that are of type SOS1 (at most one nonzero), SOS2 (at most two nonzero and adjacent) and SOS3 (variables must sum to 1).
- Efficiently represent multiple choice, k out of n decisions.
- View quickly a number of optimal and near optimal solutions in multi-criteria decision-making.
- Allows much faster solution times for wide range of portfolio-like problems such as Value-at-Risk.
- The user can specify the language for the What’sBest! menu
- error messages can be specified to be in English, French or Chinese.
LINDO API allows you to easily create your own optimisation applications and plug the capabilities of the LINDO solvers right into customised applications and mathematical programs that you have written.
LINDO API 6.1 New Features
The latest release of LINDO API, version 6.1, includes the following enhancements:
New Stochastic Programming Option
New features allow modeling and optimization for models with uncertain elements via multistage stochastic linear, nonlinear and integer stochastic programming (SP). Benders decomposition is used for solving large linear SP models. Deterministic equivalent method is used for solving nonlinear and integer SP models. Support is available for over 20 distribution types (discrete or continuous). User defined functions are allowed via call-backs. Customized sampling scenarios via the statistical sampling API.
Statistical Sampling API
Extensive API functions to sample directly from various statistical distributions. Variance reduction with Latin-Hyper-Cube and Anti-thetic variates sampling. Generation of correlated samples via Pearson, Spearman, or Kendall correlation measures. Pseudo random uniform generation via a choice of three different generators.
Faster Linear Solvers
Large linear models solve an average of 20% faster with improved Primal and Dual Simplex solvers.
Substantial Integer Solver Improvements
The Integer Solver is an average of 50% faster on a broad range of integer models. Substantial improvements have been made to all heuristics for finding close to optimal solutions quickly. There have also been significant improvements in cut generation for certain types of special model structures.
Global Solver Improvements
The Global Solver has seen significant improvement in the handling of nonlinear models with quadratic terms, especially non-convex quadratic expressions.
LINGOLINGO is a comprehensive tool designed to make building and solving linear, nonlinear and integer optimization models faster, easier and more efficient. LINGO provides a completely integrated package that includes a powerful language for expressing optimization models, a full featured environment for building and editing problems, and a set of fast built-in solvers.
LINGO 12.0 New features
All New Stochastic Programming (SP) Solver
The SP solver supports decision making under uncertainty through multistage stochastic models with recourse. The user expresses the uncertainty via distribution functions, either built-in or user-defined, and the stochastic solver will optimize the model to minimize the cost of the initial stage plus the expected value of recourse decisions over the planning horizon. Advanced sampling modes are also available to approximate stochastic parameters from parametric distributions.
Other features include:
- Available for modeling linear, nonlinear and integer stochastic programs (SP).
- Supports most standard distributions, e.g., Normal, Poisson, as well as user supplied.
- Full solutions for each of the possible scenarios are available at the scripting level, (calc sections) allowing for the creation of custom reports on variable values over the full range of scenarios.
- Ability to generate and display the underlying deterministic equivalent used to optimize SP models.
- Variance reduction with Latin-Hyper-Square sampling.
- Ability to generate statistically dependent samples based on Pearson, Spearman or Kendalls correlation measures.
- Pseudorandom number generator with long cycle length and excellent high dimensional uniformity.
Global Solver Improvements
- Significant improvements in exploiting quadratic expressions, making the global solver more efficient on non-convex quadratic models, as well as general nonlinear models with quadratic terms.
- Automatic recognition of second-order cone quadratic problems, such as Value-at-Risk models, allowing for dramatically faster solution times via the barrier solver.
- Reformulation capabilities that improve performance for a wide range of composite functions.
Integer Solver Improvements
Enhancements in the feasibility-pump heuristic to help find improved feasible solutions on many difficult problems.
- Enhancements in the rounding techniques exploit an even wider range of constraint structures.
- Standard heuristics have been improved.
Improved Performance on Models with Nested Loops
Loop optimization reformulates expressions containing nested set looping functions in order to make them more efficient, while maintaining mathematical equivalency. The end goal of loop optimization is to minimize the number of passes through the inner loop of any nested loops in an expression. Improvements in model generation times for some models can be dramatic.
Simplex Solver Improvements
Large linear models solve an average of 20% faster with the enhanced dual and primal simplex solvers.
More Flexibility in Solution Report Precision
LINGO's solution reports are no longer restricted to 7 significant digits when reporting numeric results. The user may now control the degree of precision, with anywhere from 1 to 17 significant digits.
New Scripting Function Capabilities
A number of calc section scripting functions were added or improved:
@GENDUAL generates the dual formulation of a linear program.
@FORMAT can now format output of strings, as well as numeric values.
@SMPS generates MPS format model files.
Variable Name Lengths Extended
Prior releases of LINGO had a limit of 32 characters on variable name lengths. This limit has been increased to 64 characters.