Results 161 to 170 of about 39,293 (207)

Mixed-Integer Linear Programming Formulations

2014
In this chapter, (mixed-)integer linear programming formulations of the resource-constrained project scheduling problem are presented. Standard formulations from the literature and newly proposed formulations are classified according to their size in function of the input data.
Artigues, Christian, Kone, Oumar, Lopez, Pierre, Mongeau, Marcel   +3 more
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The Mixed Integer Linear Bilevel Programming Problem

Operations Research, 1990
A two-person, noncooperative game in which the players move in sequence can be modeled as a bilevel optimization problem. In this paper, we examine the case where each player tries to maximize the individual objective function over a jointly constrained polyhedron. The decision variables are variously partitioned into continuous and discrete sets. The
James T. Moore, Jonathan F. Bard
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An optimality cut for mixed integer linear programs

European Journal of Operational Research, 1999
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Gilbert Laporte, Frédéric Semet
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A DC Programming Approach for Mixed-Integer Linear Programs

2008
In this paper, we propose a new efficient algorithm for globally solving a class of Mixed Integer Program (MIP). If the objective function is linear with both continuous variables and integer variables, then the problem is called a Mixed Integer Linear Program (MILP). Researches on MILP are important in both theoretical and practical aspects.
Yi-Shuai Niu, Pham Dinh Tao
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Bivium as a Mixed-Integer Linear Programming Problem

2009
Trivium is a stream cipher proposed for the eSTREAM project. Raddum introduced some reduced versions of Trivium, named Bivium A and Bivium B. In this article we present a numerical attack on the Biviums. The main idea is to transform the problem of solving a sparse system of quadratic equations over GF (2) into a combinatorial optimization problem.
Julia Borghoff, Lars R. Knudsen, Mathias Stolpe   +2 more
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From Mixed-Integer Linear to Mixed-Integer Bilevel Linear Programming

2017
Bilevel Optimization is a very challenging framework where two players (with different objectives) compete for the definition of the final solution. In this paper we address a generic mixed-integer bilevel linear program, i.e., a bilevel optimization problem where the objective functions and constraints are all linear, and some variables are required ...
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Marginal values in mixed integer linear programming

Mathematical Programming, 1989
Marginal values of a given optimization problem are the directional partial derivatives of the value with respect to perturbations in the data. If \(v(c,A,b)=\min \{cx|\) Ax\(\geq b\), \(x\geq 0\}\) and if \(u=(c',A',b')\) is a vector, then the marginal value in direction u is defined by \[ \frac{\partial v}{\partial u}=\lim_{\epsilon \to 0+}\frac{v(c+\
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Guaranteed Outlier Removal with Mixed Integer Linear Programs

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016
The maximum consensus problem is fundamentally important to robust geometric fitting in computer vision. Solving the problem exactly is computationally demanding, and the effort required increases rapidly with the problem size. Although randomized algorithms are much more efficient, the optimality of the solution is not guaranteed.
Tat-Jun Chin, Yang Heng Kee, Anders P. Eriksson, Frank Neumann 0001   +3 more
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Linear and Mixed Integer Programming for Portfolio Optimization

2015
This book presents solutions to the general problem of single period portfolio optimization. It introduces different linear models, arising from different performance measures, and the mixed integer linear models resulting from the introduction of real features.
MANSINI, Renata, Ogryczak, Włodzimierz, SPERANZA, Maria Grazia   +2 more
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Generative Branching for Mixed-Integer Linear Programming

Proceedings of the AAAI Conference on Artificial Intelligence
Branch-and-bound (B&B) is a fundamental algorithmic framework for solving Mixed-Integer Linear Programming (MILP) problems, where branching decisions critically affect solver efficiency. Recent learning-based methods apply imitation learning to select branching variables, but their deterministic predictions limit exploration and generalization.
Ruobing Wang, Xin Li, Yangchuan Wang, Zijian Zhang, Mingzhong Wang   +4 more
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