mixed-integer programming

MIPLIB 2017

The newest release of the Mixed Integer Programming Library

From feasibility to improvement to proof: three phases of solving mixed-integer programs

Modern mixed-integer programming (MIP) solvers employ dozens of auxiliary algorithmic components to support the branch-and-bound search in finding and improving primal solutions and in strengthening the dual bound. Typically, all components are tuned …

Exploiting Solving Phases for Mixed-Integer Programs

Modern MIP solving software incorporates dozens of auxiliary algorithmic components for supporting the branch-and-bound search in finding and improving solutions and in strengthening the relaxation. Intuitively, a dynamic solving strategy with an …

Enhancing MIP Branching Decisions by Using the Sample Variance of Pseudo Costs

The selection of a good branching variable is crucial for small search trees in Mixed Integer Programming. Most modern solvers employ a strategy guided by history information, mainly the variable pseudo-costs, which are used to estimate the objective …

Empirical Analysis of Solving Phases in Mixed Integer Programming

Modern solving software for mixed-integer programming (MIP) incorporates numerous algorithmic components whose behavior is controlled by user parameter choices, and whose usefulness dramatically varies depending on the progress of the solving …