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Gotowe bibliografie tematyczne / Mixed-Integer Linear Programs

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Gotowa bibliografia na temat „Mixed-Integer Linear Programs”

Autor: Grafiati

Data publikacji: 6 września 2023

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Artykuły w czasopismach na temat "Mixed-Integer Linear Programs"

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Guieu, Olivier, and John W. Chinneck. "Analyzing Infeasible Mixed-Integer and Integer Linear Programs." INFORMS Journal on Computing 11, no. 1 (1999): 63–77. http://dx.doi.org/10.1287/ijoc.11.1.63.

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Cornuéjols, Gérard. "Valid inequalities for mixed integer linear programs." Mathematical Programming 112, no. 1 (2007): 3–44. http://dx.doi.org/10.1007/s10107-006-0086-0.

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Berthold, Timo, and Gregor Hendel. "Learning To Scale Mixed-Integer Programs." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 5 (2021): 3661–68. http://dx.doi.org/10.1609/aaai.v35i5.16482.

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Many practical applications require the solution of numerically challenging linear programs (LPs) and mixed integer programs (MIPs). Scaling is a widely used preconditioning technique that aims at reducing the error propagation of the involved linear systems, thereby improving the numerical behavior of the dual simplex algorithm and, consequently, LP-based branch-and-bound. A reliable scaling method often makes the difference whether these problems can be solved correctly or not. In this paper, we investigate the use of machine learning to choose at the beginning of the solution process betwee

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Laporte, Gilbert, and Frédéric Semet. "An optimality cut for mixed integer linear programs." European Journal of Operational Research 119, no. 3 (1999): 671–77. http://dx.doi.org/10.1016/s0377-2217(98)00357-9.

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Proll, L. G. "Stronger formulations of mixed integer linear programs: an example." International Journal of Mathematical Education in Science and Technology 28, no. 5 (1997): 707–12. http://dx.doi.org/10.1080/0020739970280507.

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Klotz, Ed, and Alexandra M. Newman. "Practical guidelines for solving difficult mixed integer linear programs." Surveys in Operations Research and Management Science 18, no. 1-2 (2013): 18–32. http://dx.doi.org/10.1016/j.sorms.2012.12.001.

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Bonami, Pierre, Gérard Cornuéjols, Sanjeeb Dash, Matteo Fischetti, and Andrea Lodi. "Projected Chvátal–Gomory cuts for mixed integer linear programs." Mathematical Programming 113, no. 2 (2006): 241–57. http://dx.doi.org/10.1007/s10107-006-0051-y.

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Pryor, Jennifer, and John W. Chinneck. "Faster integer-feasibility in mixed-integer linear programs by branching to force change." Computers & Operations Research 38, no. 8 (2011): 1143–52. http://dx.doi.org/10.1016/j.cor.2010.10.025.

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Chen, Binyuan, Simge Küçükyavuz, and Suvrajeet Sen. "Finite Disjunctive Programming Characterizations for General Mixed-Integer Linear Programs." Operations Research 59, no. 1 (2011): 202–10. http://dx.doi.org/10.1287/opre.1100.0882.

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Li, Xiaobo, Karthik Natarajan, Chung-Piaw Teo, and Zhichao Zheng. "Distributionally robust mixed integer linear programs: Persistency models with applications." European Journal of Operational Research 233, no. 3 (2014): 459–73. http://dx.doi.org/10.1016/j.ejor.2013.07.009.

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Rozprawy doktorskie na temat "Mixed-Integer Linear Programs"

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Guieu, Olivier Carleton University Dissertation Engineering Systems and Computer. "Analyzing infeasible mixed-integer and integer linear programs." Ottawa, 1995.

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Yildiz, Sercan. "Valid Inequalities for Mixed-Integer Linear and Mixed-Integer Conic Programs." Research Showcase @ CMU, 2016. http://repository.cmu.edu/dissertations/777.

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Mixed-integer programming provides a natural framework for modeling optimization problems which require discrete decisions. Valid inequalities, used as cutting-planes and cuttingsurfaces in integer programming solvers, are an essential part of today’s integer programming technology. They enable the solution of mixed-integer programs of greater scale and complexity by providing tighter mathematical descriptions of the feasible solution set. This dissertation presents new structural results on general-purpose valid inequalities for mixedinteger linear and mixed-integer conic programs. Cut-genera

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Nwana, Vincent Lebga. "Parallel algorithms for solving mixed integer linear programs." Thesis, Brunel University, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.368540.

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Salvagnin, Domenico. "Constraint Programming Techniques for Mixed Integer Linear Programs." Doctoral thesis, Università degli studi di Padova, 2009. http://hdl.handle.net/11577/3425690.

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Many decision problems in industry, logistics, and telecommunications can be viewed as satisfiability or optimization problems. Two paradigms have reached a high degree of sophistication from the point of view of both theory and implementation: Constraint Programming (CP) and Mixed Integer Programming (MIP). The CP and MIP paradigms have strengths and weaknesses that complement each other. On the one hand, CP, through the use of sophisticated propagation techniques, privileges primal inference. On the other hand, MIP, through the techniques of relaxation and strengthening through cutting plane

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Köppe, Matthias [Verfasser]. "Exact Primal Algorithms for General Integer and Mixed-Integer Linear Programs / Matthias Köppe." Aachen : Shaker, 2003. http://d-nb.info/1179032292/34.

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Chen, Binyuan. "FINITE DISJUNCTIVE PROGRAMMING METHODS FOR GENERAL MIXED INTEGER LINEAR PROGRAMS." Diss., The University of Arizona, 2011. http://hdl.handle.net/10150/145120.

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In this dissertation, a finitely convergent disjunctive programming procedure, the Convex Hull Tree (CHT) algorithm, is proposed to obtain the convex hull of a general mixed–integer linear program with bounded integer variables. The CHT algorithm constructs a linear program that has the same optimal solution as the associated mixed-integer linear program. The standard notion of sequential cutting planes is then combined with ideasunderlying the CHT algorithm to help guide the choice of disjunctions to use within a new cutting plane method, the Cutting Plane Tree (CPT) algorithm. We show that t

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Smith, Edmund. "Parallel solution of linear programs." Thesis, University of Edinburgh, 2013. http://hdl.handle.net/1842/8833.

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The factors limiting the performance of computer software periodically undergo sudden shifts, resulting from technological progress, and these shifts can have profound implications for the design of high performance codes. At the present time, the speed with which hardware can execute a single stream of instructions has reached a plateau. It is now the number of instruction streams that may be executed concurrently which underpins estimates of compute power, and with this change, a critical limitation on the performance of software has come to be the degree to which it can be parallelised. The

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Ramakrishnan, V. S., and Jeremy F. 1939 Shapiro. "Analyzing Multi-Objective Linear and Mixed Integer Programs by Lagrange Multipliers." Massachusetts Institute of Technology, Operations Research Center, 1991. http://hdl.handle.net/1721.1/5322.

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A new method for multi-objective optimization of linear and mixed programs based on Lagrange multiplier methods is developed. The method resembles, but is distinct from, objective function weighting and goal programming methods. A subgradient optimization algorithm for selecting the multipliers is presented and analyzed. The method is illustrated by its application to a model for determining the weekly re-distribution of railroad cars from excess supply areas to excess demand areas, and to a model for balancing cost minimization against order completion requirements for a dynamic lot size mode

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Adams, Warren Philip. "The mixed-integer bilinear programming problem with extensions to zero-one quadratic programs." Diss., Virginia Polytechnic Institute and State University, 1985. http://hdl.handle.net/10919/74711.

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This research effort is concerned with a class of mathematical programming problems referred to as Mixed-Integer Bilinear Programming Problems. This class of problems, which arises in production, location-allocation, and distribution-application contexts, may be considered as a discrete version of the well-known Bilinear Programming Problem in that one set of decision variables is restricted to be binary valued. The structure of this problem is studied, and special cases wherein it is readily solvable are identified. For the more general case, a new linearization technique is introduced and de

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Gayen, Neela. "Automatic parallelization of stream programs for resource efficient embedded processors." Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/213058/1/Neela_Gayen_Thesis.pdf.

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This thesis considers how to exploit the specific characteristics of data streaming functions and multi-core processors to increase throughput through appropriate software process mappings. The hypothesis is that large numbers of low-power processors can achieve high throughput for streaming applications if a good mapping is provided. The innovation is to use compilation principles to guide the mapping, rather than heuristics. Three increasingly complex approaches are developed that focus on computational bottlenecks, then adds communication overheads, and lastly adds the costs of splitting

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Części książek na temat "Mixed-Integer Linear Programs"

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Qualizza, Andrea, Pietro Belotti, and François Margot. "Linear Programming Relaxations of Quadratically Constrained Quadratic Programs." In Mixed Integer Nonlinear Programming. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1927-3_14.

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Niu, Yi-Shuai, and Tao Pham Dinh. "A DC Programming Approach for Mixed-Integer Linear Programs." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-87477-5_27.

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Johnson, Ellis L. "Modeling and Strong Linear Programs for Mixed Integer Programming." In Algorithms and Model Formulations in Mathematical Programming. Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-83724-1_1.

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Walter, Matthias. "Face Dimensions of General-Purpose Cutting Planes for Mixed-Integer Linear Programs." In Integer Programming and Combinatorial Optimization. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73879-2_28.

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Gomory, Ralph E. "Outline of an Algorithm for Integer Solutions to Linear Programs and An Algorithm for the Mixed Integer Problem." In 50 Years of Integer Programming 1958-2008. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-68279-0_4.

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Jeong, Jihwan, Scott Sanner, and Akshat Kumar. "A Mixed-Integer Linear Programming Reduction of Disjoint Bilinear Programs via Symbolic Variable Elimination." In Integration of Constraint Programming, Artificial Intelligence, and Operations Research. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-33271-5_6.

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Güney, Evren. "A Mixed Integer Linear Program for Election Campaign Optimization Under D’Hondt Rule." In Operations Research Proceedings. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-89920-6_11.

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Mouret, Sylvain, Ignacio E. Grossmann, and Pierre Pestiaux. "Tightening the Linear Relaxation of a Mixed Integer Nonlinear Program Using Constraint Programming." In Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01929-6_16.

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Frisch, Sarah, Philipp Hungerländer, Anna Jellen, and Dominic Weinberger. "A Mixed Integer Linear Program for Optimizing the Utilization of Locomotives with Maintenance Constraints." In Operations Research Proceedings. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18500-8_14.

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Westerlund, J., and L. G. Papageorgiou. "Comparison of Some Mixed Integer Non-linear Solution Approaches Applied to Process Plant Layout Problems." In Progress in Industrial Mathematics at ECMI 2004. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/3-540-28073-1_48.

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Streszczenia konferencji na temat "Mixed-Integer Linear Programs"

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Chin, Tat-Jun, Yang Heng Kee, Anders Eriksson, and Frank Neumann. "Guaranteed Outlier Removal with Mixed Integer Linear Programs." In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2016. http://dx.doi.org/10.1109/cvpr.2016.631.

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Gupta, Vijay, and Ignacio E. Grossmann. "Computational Strategies for Multistage Mixed-Integer Linear Stochastic Programs with Endogenous Uncertainty." In A Special Workshop of the Stochatic Programming Community and the European Association of Operational Research Societies (EURO) on "Stochastic Programming for Implementation and Advanced Applications". The Association of Lithuanian Serials, 2012. http://dx.doi.org/10.5200/stoprog.2012.06.

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Sadat, Sayed A., and Lingling Fan. "Mixed integer linear programming formulation for chance constrained mathematical programs with equilibrium constraints." In 2017 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2017. http://dx.doi.org/10.1109/pesgm.2017.8273875.

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Butter, Thomas, Franz Rothlauf, Jörn Grahl, Tobias Hildenbrand, and Jens Arndt. "Genetic algorithms and mixed integer linear programs for optimal strategies in a student's "sports" activity." In the 8th annual conference. ACM Press, 2006. http://dx.doi.org/10.1145/1143997.1144296.

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Kouider, Ahmed, Hacene Ait Haddadene, Samia Ourari, and Ammar Oulamara. "Mixed integer linear programs and tabu search approach to solve mixed graph coloring for unit-time job shop scheduling." In 2015 IEEE International Conference on Automation Science and Engineering (CASE). IEEE, 2015. http://dx.doi.org/10.1109/coase.2015.7294257.

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Akintunde, Michael E., Elena Botoeva, Panagiotis Kouvaros, and Alessio Lomuscio. "Verifying Strategic Abilities of Neural-symbolic Multi-agent Systems." In 17th International Conference on Principles of Knowledge Representation and Reasoning {KR-2020}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/kr.2020/3.

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We investigate the problem of verifying the strategic properties of multi-agent systems equipped with machine learning-based perception units. We introduce a novel model of agents comprising both a perception system implemented via feed-forward neural networks and an action selection mechanism implemented via traditional control logic. We define the verification problem for these systems against a bounded fragment of alternating-time temporal logic. We translate the verification problem on bounded traces into the feasibility problem of mixed integer linear programs and show the soundness and c

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Iranmanesh, Ehsan, and Ramesh Krishnamurti. "Mixed Integer Program Heuristic for Linear Ordering Problem." In 5th International Conference on Operations Research and Enterprise Systems. SCITEPRESS - Science and Technology Publications, 2016. http://dx.doi.org/10.5220/0005710701520156.

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Shao, Yufen, Jizhou Li, Ming–Jung Seow, et al. "Integrated Concept Analytics and Development Optimization Under Uncertainties." In ADIPEC. SPE, 2022. http://dx.doi.org/10.2118/211442-ms.

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Abstract Decision-making complexity in the oil and gas industry has risen dramatically in recent years, especially in consideration of uncertainties related to geopolitics, policies, marketing, subsurface resources etc. To enable decision making with the best quality opportunities and projects, we are developing an integrated suite of machine augmented mathematical technologies to recommend holistic decisions for concept selection and development planning under uncertainties. Our ongoing technology development is progressing a set of prototypes and use cases including: 1) AI-based uncertainty

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Guirguis, David, David A. Romero, and Cristina H. Amon. "Efficient Wind Turbine Micrositing in Large-Scale Wind Farms." In ASME 2016 10th International Conference on Energy Sustainability collocated with the ASME 2016 Power Conference and the ASME 2016 14th International Conference on Fuel Cell Science, Engineering and Technology. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/es2016-59594.

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As wind energy is established as a sustainable alternative source of electricity, very large-scale wind farms with hundreds of turbines are becoming increasingly common. For the optimal design of wind farm layouts, the number of decision variables is at least twice the number of turbines (e.g., the Cartesian coordinates of each turbine). As the number of turbines increases, the computational cost incurred by the optimization solver to converge to a satisfactory solution increases as well. This issue represents a serious limitation in the computer-aided design of large wind farms. Moreover, the

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Bae, Su W., Jong Park, and John-Paul Clarke. "Modified Mixed Integer Linear Program for Airport Departure Scheduling." In AIAA Guidance, Navigation, and Control (GNC) Conference. American Institute of Aeronautics and Astronautics, 2013. http://dx.doi.org/10.2514/6.2013-4885.

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