This research paper presents an empirical evaluation of the intersection of combinatorial optimization and machine learning, aiming to provide valuable guidance to the research community. The authors emphasize the importance of communicating both the strengths and weaknesses of proposed approaches.
Authors: Ankur Nath, Department of Computer Science and Engineering, Texas A&M University; Alan Kuhnle, Department of Computer Science and Engineering, Texas A&M University.
Cplex. https://www.ibm.com/products/ilog-cplexoptimization-studio/cplex-optimizer. Dong, Y., Goldberg, A. V., Noe, A., Parotsidis, N., Resende, M. G., and Spaen, Q. . New instances for maximum weight independent set from a vehicle routing application. In Operations Research Forum, volume 2, pages 1–6. Springer. E´en, N. and S¨orensson, N. . An extensible satsolver. In International conference on theory and applications of satisfiability testing, pages 502–518. Springer. Elsokkary, N.
transactions on evolutionary computation, 1:67–82. Ye, Y. . The gset dataset. https://web.stanford.edu/ yyye/yyye/Gset/. Yolcu, E. and P´oczos, B. . Learning local search heuristics for boolean satisfiability. Advances in Neural Information Processing Systems, 32. This paper is available on arxiv under CC 4.0 DEED license.
Cplex. https://www.ibm.com/products/ilog-cplexoptimization-studio/cplex-optimizer. Dong, Y., Goldberg, A. V., Noe, A., Parotsidis, N., Resende, M. G., and Spaen, Q. . New instances for maximum weight independent set from a vehicle routing application. In Operations Research Forum, volume 2, pages 1–6. Springer. E´en, N. and S¨orensson, N. . An extensible satsolver. In International conference on theory and applications of satisfiability testing, pages 502–518. Springer. Elsokkary, N.
Combinatorial Optimization Machine Learning Empirical Evaluation Research Strengths Weaknesses
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