পদ্ধতির তুলনা করুন

	বেয়েশীয় মিশ্র-পূর্ণসংখ্যা প্রোগ্রামিং	অনিশ্চয়তার অধীনে পূর্ণসংখ্যার চলক সহ শক্তিশালী মিশ্র-পূর্ণসংখ্যা প্রোগ্রামিং
ক্ষেত্র	অনুকরণ	অনুকরণ
পরিবার	Process / pipeline	Process / pipeline
উদ্ভবের বছর	2018 (surrogate-BO-MIP synthesis); MIP foundations 1958	1998–2004
প্রবর্তক	Baptista, R. & Poloczek, M. (formal Bayesian-BO-MIP formulation); mixed-integer programming roots in Gomory (1958)	Ben-Tal & Nemirovski; Bertsimas & Sim
ধরন	Surrogate-assisted combinatorial optimization	Deterministic robust reformulation of MIP under uncertainty
মৌলিক উৎস	Baptista, R., Poloczek, M. (2018). Bayesian Optimization of Combinatorial Structures. Proceedings of the 35th International Conference on Machine Learning (ICML), PMLR 80:462–471.	Bertsimas, D., Sim, M. (2004). The price of robustness. Operations Research, 52(1), 35–53.
অপর নাম	Bayesian MIP, BO-MIP, Bayesian Combinatorial Optimization, Mixed-Integer Bayesian Optimization	RMIP, Robust MIP, Uncertain MIP, Robust MILP/MIQP
সম্পর্কিত	5	4
সারসংক্ষেপ	Bayesian Mixed-Integer Programming (BO-MIP) couples a probabilistic surrogate model — typically a Gaussian process — with a mixed-integer programming solver to efficiently optimize expensive black-box objectives defined over spaces that contain both continuous and discrete or integer-valued decision variables. It is especially valuable when each function evaluation is costly and exhaustive search is infeasible.	Robust Mixed-Integer Programming (RMIP) combines mixed-integer programming with robust optimization to find solutions that remain feasible and near-optimal despite uncertain parameters. Instead of assuming fixed data, it protects decisions against adversarial or worst-case realizations of uncertain inputs, using an explicit uncertainty set to control the degree of conservatism while preserving the combinatorial structure of integer decisions.