A comparative analysis of optimization solvers

References

• H. D. Mittelmann, The state-of-the-art in conic optimization software, In: M. F Anjos and J. B. Lasserre (Eds.) Operations Research & Management Science, pp. 671–686, 2011.
   Google Scholar
• R. E. Bixby, A brief history of linear and mixed-integer programming computation, Documenta Mathematica, 2012, pp. 107–121.
   Google Scholar
• E. D. Andersen, C. Ross and T. Terlaky, On implementing a primaldual interior-point method ffor conic quadratic optimization, Mathematical Programming, Vol. 95 (2003), pp. 249–277. doi: 10.1007/s10107-002-0349-3
   Web of Science ®Google Scholar
• R. H. Byrd, J. Nocedal, and R. Waltz, KNITRO: An integrated package for nonlinear optimization, In: G. di Pillo and M. Roma (Eds.) Large-Scale Nonlinear Optimization, pp. 35–59, 2006.
   Google Scholar
• Y. Lin and L. Schrage. The global solver in the LINDO API, Optimization Methods & Software, Vol. 24 (2009), pp. 657–668. doi: 10.1080/10556780902753221
   Web of Science ®Google Scholar
• I. P. Androulakis, C.D. Maranas and C.A. Floudas, alphaBB: A global optimization method for general constrained nonconvex problems, J. Global Optimization, Vol. 7 (1995), pp.1–27. doi: 10.1007/BF01099647
   Web of Science ®Google Scholar
• N. V. Sahinidis, BARON: A general purpose gloal optimization software package. J. Global Optimization, Vol. 8 (1996), pp. 201–205. doi: 10.1007/BF00138693
   Web of Science ®Google Scholar
• D. Henrion, J. B. Lasserre, Solving nonconvex optimization problems-How GloptPloy is applied to problems in robust and nonlinear control. IEEE Control Systems Magazine, Vol. 24 (2004), pp. 72–83. doi: 10.1109/MCS.2004.1299534
   Web of Science ®Google Scholar
• M. Schlueter, S. O. Erb, M. Gerdts, S. Kemble, J.J. Rueckmann, MIDACO on MINLP space applications, Advances in Space Research, Vol. 51 (2013), pp. 1116–1131. doi: 10.1016/j.asr.2012.11.006
   Web of Science ®Google Scholar
• Wu, Z. Y., Yang, Y. J., & Bai, F. S. (2009). A filled function method for quadratic programs with binary constraints. Optimization, 58(5), 585–598. doi: 10.1080/02331930902928757
   Web of Science ®Google Scholar
• C. Bliek, P. Bonami and A. Lodi, Solving mixed-integer quadratic programming problems with IBM-CPLEX: a progress report, In: Proc. RAMP Symposium, pp. 171–180, 2014, Tokyo.
   Google Scholar
• L. L. Sun, W. Liu, and T. Y. Chai, Tool sequence optimization based on three logical modes of robot control via CPLEX optimization. J. Shanghai Jiaotong University, Vol.16 (2011), pp. 436–440. doi: 10.1007/s12204-011-1171-y
   Google Scholar
• R. Fourer, Using AMPL with Guroi to apply optimization models efficiently and reliably, In: Gurobi Optimizer Solution Seminar, pp. 1–52, 2011, Tokyo.
   Google Scholar
• R. Laundy, M. Perregaard, G. Tavares, H. Tipi, and A. Vazacopoulos, Solving hard mixed integer programming problems with Xpress-MP: A MIPLIB 2003 case study, Informs Journal on Computing, Vol. 21 (2009), pp. 304–313. doi: 10.1287/ijoc.1080.0293
   Web of Science ®Google Scholar
• T. Berthold, J. Farmer, S. Heinz, and M. Perregaard, Paralleization of the FICO Xpress-Optimizer, In: International Congress on Mathematical Software, pp. 251–258, 2016, Cham.
   Google Scholar
• V. Kumar and D. Kumar, An Astrophysics-inspired Grey Wolf Algorithm for Numerical Optimization and its Application to Engineering Design Problems, Advances in Engineering Software, Vol. 112(2017), pp. 231–254. doi: 10.1016/j.advengsoft.2017.05.008
   Web of Science ®Google Scholar
• G.-W. Weber, Generalized semi-infinite optimization: theory and applications in optimal control and discrete optimization, Journal of Statistics and Management Systems, Vol. 5 (2002), pp. 359–388. doi: 10.1080/09720510.2002.10701064
   Google Scholar
• M. Serafini, F. Furini, G. Colombo & C, Rizzi, Optimized development: defining design rules through product optimization techniques, Computer-Aided Design and Applications, Vol. 13 (2016), pp. 600–609. doi: 10.1080/16864360.2016.1150704
   Google Scholar
• C.-Y. Wang, W. Zhao, Q. Liu & H.-W. Chen, Optimization of the tool selection based on big data, Journal of Discrete Mathematical Sciences and Cryptography, Vol. 20(2017), pp.341–360. doi: 10.1080/09720529.2016.1183310
   Web of Science ®Google Scholar