Deterministic global optimization with Gaussian processes embedded

DOI10.1007/s12532-021-00204-yzbMath1476.90270arXiv2005.10902OpenAlexW3175956409MaRDI QIDQ2062323

Artur M. Schweidtmann, Dominik Bongartz, Jaromił Najman, Tim Kerkenhoff, Xiaopeng Lin, Daniel Grothe, Alexander Mitsos

Publication date: 27 December 2021

Published in: Mathematical Programming Computation (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2005.10902

zbMATH Keywords

krigingchance-constrained programmingmachine learningexpected improvementBayesian optimizationreduced-spaceacquisition function

Mathematics Subject Classification ID

Applications of mathematical programming (90C90) Nonconvex programming, global optimization (90C26) Nonlinear programming (90C30) General topics in artificial intelligence (68T01) Software, source code, etc. for problems pertaining to probability theory (60-04)

Related Items (6)

Obey validity limits of data-driven models through topological data analysis and one-class classification ⋮ Surrogate-based branch-and-bound algorithms for simulation-based black-box optimization ⋮ Comparison of MINLP formulations for global superstructure optimization ⋮ Convex and concave envelopes of artificial neural network activation functions for deterministic global optimization ⋮ Improved convex and concave relaxations of composite bilinear forms ⋮ MeLOn

Uses Software

• BARON
• TOMP
• SCIP
• SNOPT
• CPLEX
• FADBAD++
• CLP
• filib++
• ANTIGONE
• NLopt
• ARGONAUT
• MAiNGO
• libALE

Cites Work

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