Lingo 13.0

This speeds the proof of global optimality. Contact us Privacy policy FAQ. Improved warm-start in solving multistage SPs.

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This is opposed to a model that contains a high degree of simultaneity that can't be permuted into lower triangular form. Improved method to induce correlations among stochastic parameters.

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Significant improvements in root node heuristics for quickly finding good, integer-feasible solutions. This speeds the proof of global optimality.

Improved ability to identify constraints that can be reformulated as conic i. Improved heuristics for finding a good, feasible solution quickly. Improved warm-start in solving multistage SPs. Improved bounds for non-convex quadratic terms using SDP and eigenvalue reformulations. If a matrix is mostly lower triangular, then, in general, the model should prove easier to solve. Multiple attributes may be displayed in a single chart, with each drawn in a different color.

Allowing certain constraints to be violated with low probability can be a reasonable and practical strategy. Specify Variable Branching Priority: Improved ability for efficiently handling polynomial terms.

The LINGO API supports new function calls for retrieving variable values on the fly in the callback function, as well as a function to load a license directly from a string. A solution that satisfies all possible outcomes can be prohibitively expensive, or even impossible.

Charts may be displayed in either two or three dimensions. The charting capability has been extensively updated: CCP is useful when certain resources or demands are random. Contact us Privacy policy FAQ.

Linyo may now be flagged as being convex, in cases where the constraint's complexity makes it impossible for the global solver to automatically determine convexity. You may now choose to have a model's underlying matrix displayed in permuted format, where the rows and columns are automatically permuted to place the matrix into mostly lower-triangular form.

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Improved identification of special structures in certain classes of models, as in multi-period models, and the ability to exploit this structure to achieve significant reductions in solve times. The encryption algorithm has been significantly strengthened, and encrypted model fragments may also be merged into a single model at runtime. In chance-constrained programming CCPone or more sets of constraints are allowed to be violated with a specified probability.

Support of Chance-Constrained Programs:

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