Release 10 of LINDO API includes a wide range of performance enhancements and new features.

Faster Solutions on Linear Models with Improved Simplex Solver

Enhancements to the Simplex solvers boost performance on large linear models. Large models solve an average of 35% faster using primal simplex and 20% faster for dual simplex.
Additional LP solver enhancements include a new extension to support multiple objective criteria.

Improved Integer Solver with new features

A new optimization mode has been introduced to ensure reproducibility of runs. 
Investigate alternative optima more quickly. Enhancements to the K-Best algorithm allow finding K best solutions in little more time than finding one solution. 
Find faster solutions to models with knapsack constraints and block structures using new heuristic algorithms. 
New preprocessing level tightens variable bounds for better performance on classes of nonlinear models.

Enhanced Stochastic Solver

Large linear multistage SP instances solve 60% faster with improved cut management for Nested Benders Decomposition Method.
Better handling of multistage SP models which do not have full-recourse. 
Extensions to the parser allow the use of arbitrarily complex functions of stochastic parameters.

Improved Global Solver

Performance of Global solver has been dramatically improved on classes of quadratic problems. In particular, non-convex quadratic problems rejected by other solvers, or otherwise solvable only slowly to a local optimum by traditional NLP solvers. Can solve some previously intractable problems to global optimality, especially financial portfolio models with minimum buy quantities, and/or limit on number of instruments at nonzero level.
Incorporates a new bound tightening process to the linearization procedure and improves solvability of linearized model. Dramatically faster, more robust performance on many models with functions like MAX( ), MIN( ), ABS( ), x*z where z = 0 or 1, etc.

Additional Enhancements

Extended collection of user callable matrix operations for working with covariance matrices such as in financial portfolio design. Routines include general eigenvalue decomposition, cholesky factorization for generating correlated random variables, Semi-Definiteness (SDP) constraints, plus linear regression, e.g. for doing demand forecasting for production planning models.

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