The encryption algorithm has been significantly strengthened, and encrypted model fragments may also be merged into a single model at runtime. The charting capability has been extensively updated: This speeds the proof of global optimality. Specify Variable Branching Priority: Improved bounds for non-convex quadratic terms using SDP and eigenvalue reformulations. CCP is useful when certain resources or demands are random.
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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. Improved ability to identify constraints that can be reformulated as conic i.
CCP is useful when certain resources or demands are random. 13. encryption algorithm has been significantly strengthened, and encrypted model fragments may lingo 13.0 be merged into a single model at runtime. Improved warm-start in solving multistage SPs. 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.
Improved ability for efficiently handling polynomial terms. If a matrix is mostly lower triangular, then, in general, the model should prove easier to solve. 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 lingo 13.0 from a string.
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Multiple attributes libgo be displayed in a single chart, with each drawn in a different color. Lingo 13.0 heuristics for finding a good, feasible solution quickly. This speeds the proof of global optimality. Charts may be displayed in either two or three dimensions. A solution that satisfies all possible outcomes can be prohibitively expensive, or even impossible.
Specify Lingo 13.0 Branching Priority: In chance-constrained programming CCPone or more sets of constraints are allowed to be violated with a specified probability.
Improved method to induce correlations among stochastic parameters. Improved bounds for non-convex quadratic terms using SDP and eigenvalue reformulations. Support of Chance-Constrained Programs: Allowing certain constraints to be violated with low probability can be a reasonable and practical strategy.
The charting capability has been extensively updated: Significant improvements in root node heuristics for linvo finding good, integer-feasible solutions. Constraints 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.