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A distributed decomposition algorithm for solving large-scale mixed integer programming problem
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DOI：10.1007/s11432-024-4210-2.png)
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Abstract
En 中文
Mixed integer programming is inherently involved in solving a significant number of practical problems. This paper focuses on mixed integer programming, where the objective function is the summation of N functions, and the constraints include both scalar coupling and set constraints. Given the potentially large scale of these problems, the goal of this work is to propose a distributed method to solve large-scale problems more efficiently. The right-hand side allocation decomposition approach is employed to address the large-scale mixed integer programming problem. Algorithms are then proposed for solving these problems, based on the analysis of the continuity, differentiability, and local convexity properties of the decomposed subproblems. Simulation experiments with randomly generated coefficients demonstrate the superior performance of the proposed algorithms compared to the Gurobi solver, offering higher solution accuracy and faster processing time for large-scale mixed integer programming problems with nonlinear objective and constraint functions.
Keywords:
mixed integer programming
decomposition methods
distributed optimization
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4.9K
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