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LINDO API 12 Enhancements

Release 12 of LINDO API includes a wide range of performance enhancements and new features.

Better Performance on Linear Models with Improved Simplex Solvers
Enhancements to the Simplex solvers boost speed on linear models. Models solve an average of 18% faster using primal simplex and 15% faster for dual simplex.

Significantly Faster on Large Quadratic Models
Improvements in handling large quadratic matrices, e.g. 1000 x 1000, provide substantial performance improvement solving financial portfolio and other quadratic models.

Improved Handling of Models with Discontinuous Functions
Models that use discontinuous functions such as MOD(x,k), INT(x), ROUND(x), can be solved more quickly.

Faster When Simultaneously Solving LPs Using Different Solvers
In situations in which it may be difficult to predict whether the Primal, Dual or Barrier Solver will provide the fastest solution to a linear model, an improved Multi-Core feature allows you to assign each solver to a separate core and run them concurrently. So, you are ensured the solution in the shortest time regardless of which solver is fastest.

Multi-Start NLP Solver Now Supports a Target Value
Users can specify a target value for the objective function. As soon as any multi-start thread achieves a specified target all threads stop.

New Engineering Design Functions Added
Support has been added for new arithmetic functions, SIGNPOWER, LMTD, RLMTD, that can be useful in various engineering design, e.g., nonlinear network and heat exchanger models.

LINDO API 11.0版增强

Release 11 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 linear models. Large models solve an average of 20% faster using primal simplex and 15% faster for dual simplex.

Improved Integer Solver

New symmetry detection capabilities dramatically reduce the time required to prove optimality on certain classes of models with integer variables. Performance has been improved on Markowitz portfolio problems with minimum buy quantities, and/or limit on number of instruments at nonzero level. Other enhancements provide faster solutions on certain task assignment-like models.

Global Solver Enhancements

Stability and robustness of the Global solver has been improved through several enhancements to quadratic recognition and range reduction. Improved exploitation of convexity of certain ratio constraints, e.g., as found in heat exchanger network design problems.

More Constraint Types Supported

Several new functions and constraint types are recognized, e.g., the AllDiff constraint for general integer variables. AllDiff constraints allow one to specify a set of integer variables, such that each variable in the set must have a unique value, different from all other variables in the set.

 

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