SCIP Solving Constraint Integer Programs
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SCIP Tags

integer solver Framework solving list constraint SharePoint constraint constraint pitch constraint describe integer Integer Converter integer sequence integer library Integer Arithmetic FIFO integer stack constraint programming constraint library Integer Number Integer Calculation Edit Integer Universal Integer convert integer to text integer vector Object Constraint Language Constraint Solver integer creator integer programming solver integer programming framework constraint integer integer programming integer linear programming integer linear integer precision arbitrary precision integer IP to Integer Integer to IP Integer Calculator Handle Unsigned Integer Unsigned Integer Char To Integer Constraint Management Software

SCIP Description

SCIP is currently one of the fastest non-commercial mixed integer programming solver. It is also a framework for Constraint Integer Programming and branch-cut-and-price. It allows total control of the solution process and the access of detailed information down to the guts of the solver. Give SCIP a try to see how it will help you in solving integer programming problems. Main features: It is a framework for branching, cutting, pricing, and propagation. It is highly flexible through many possible user plugins: constraint handlers to implement arbitrary constraints, variable pricers to dynamically create problem variables, domain propagators to apply constraint independent propagations on the variables' domains, cut separators to apply cutting planes on the LP relaxation, relaxators to provide relaxations and dual bounds in addition to the LP relaxation, primal heuristics to search for feasible solutions with specific support for probing and diving, node selectors to guide the search, branching rules to split the problem into subproblems, presolvers to simplify the solved problem, file readers to parse different input file formats, event handlers to be informed on specific events, e.g., after a node was solved, a specific variable changes its bounds, or a new primal solution is found, display handlers to create additional columns in the solver's output. dialog handlers to extend the included command shell. Every existing unit is implemented as a plugin, leading to an interface flexible enough to meet the needs of most additional user extensions. A dynamic cut pool management is included. The user may mix preprocessed and active problem variables in expressions: they are automatically transformed to corresponding active problem variables. Arbitrarily many children per node can be created, and the different children can be arbitrarily defined. It has an open LP solver support. It currently supports: CPLEX Gurobi XPress-MP Mosek SoPlex QSopt CLP. The LP relaxation need not to be solved at every single node (it can even be turned off completely, mimicing a pure constraint programming solver). Additional relaxations (e.g., semidefinite relaxations or Lagrangian relaxations) can be included, working in parallel or interleaved. Conflict analysis can be applied to learn from infeasible subproblems. Dynamic memory management reduces the number of operation system calls with automatic memory leakage detection in debug mode.

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