A Framework for Globally Optimizing Mixed-Integer Signomial Programs

A Framework for Globally Optimizing Mixed-Integer Signomial Programs

A Parallel Macro Partitioning Framework for Solving Mixed Integer Programs

Mixed Integer Programs are a class of optimization problems which have a vast range of applications in engineering, business, science, health care, and other areas. For many applications, however, problems of realistic size can take a an impractical amount of time to solve on a single workstation. However, using parallel computing resources to solve MIP is difficult, as parallelizing the standa...

A mixed integer nonlinear programming model for optimizing a gas pipeline transmission linear network

The technical equipment developed and used in both installation and operation processes in refineries, oil and gas pipelines, and gas booster stations has always been expensive. Hence, managers at different organizational levels are keen to find methods to control and reduce these costs. Generally speaking, the operators in a gas booster station choose the operating devices without considering ...

An algorithmic framework for convex mixed integer nonlinear programs

This paper is motivated by the fact that mixed integer nonlinear programming is an important and difficult area for which there is a need for developing new methods and software for solving large-scale problems. Moreover, both fundamental building blocks, namely mixed integer linear programming and nonlinear programming, have seen considerable and steady progress in recent years. Wishing to exp...

A Parametric Integer Programming Algorithm for Bilevel Mixed Integer Programs

We consider discrete bilevel optimization problems where the follower solves an integer program with a fixed number of variables. Using recent results in parametric integer programming, we present polynomial time algorithms for pure and mixed integer bilevel problems. For the mixed integer case where the leader’s variables are continuous, our algorithm also detects whether the infimum cost fail...