Data-driven spatial branch-and-bound algorithms for box-constrained simulation-based optimization
From MaRDI portal
Publication:2070360
DOI10.1007/s10898-021-01045-8zbMath1484.90091OpenAlexW4200503073MaRDI QIDQ2070360
Fani Boukouvala, Jianyuan Zhai
Publication date: 24 January 2022
Published in: Journal of Global Optimization (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10898-021-01045-8
global optimizationbranch-and-boundconvex underestimatorsblack-box optimizationsimulation-optimization
Polyhedral combinatorics, branch-and-bound, branch-and-cut (90C57) Nonconvex programming, global optimization (90C26)
Related Items (2)
Surrogate-based branch-and-bound algorithms for simulation-based black-box optimization ⋮ Branch-and-Model: a derivative-free global optimization algorithm
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Optimization by Simulated Annealing
- Global optimization advances in mixed-integer nonlinear programming, MINLP, and constrained derivative-free optimization, CDFO
- A deterministic global optimization using smooth diagonal auxiliary functions
- Efficient global optimization of expensive black-box functions
- Global optimization by multilevel coordinate search
- Lipschitzian optimization without the Lipschitz constant
- Optimization of black-box problems using Smolyak grids and polynomial approximations
- Deterministic global optimization with artificial neural networks embedded
- A polyhedral branch-and-cut approach to global optimization
- Global optimization of mixed-integer nonlinear programs: a theoretical and computational study
- \(\alpha BB\): A global optimization method for general constrained nonconvex problems
- Surrogate optimization of deep neural networks for groundwater predictions
- Deterministic global derivative-free optimization of black-box problems with bounded Hessian
- ANTIGONE: algorithms for coNTinuous/Integer global optimization of nonlinear equations
- Simulation-based optimization. Parametric optimization techniques and reinforcement learning
- ARGONAUT: algorithms for global optimization of constrained grey-box computational problems
- Derivative-free optimization: a review of algorithms and comparison of software implementations
- A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code
- On the Convergence of Pattern Search Algorithms
- Introduction to Derivative-Free Optimization
- An outer-approximation algorithm for a class of mixed-integer nonlinear programs
- Computability of global solutions to factorable nonconvex programs: Part I — Convex underestimating problems
- Feature Article—Genetic Algorithms for the Operations Researcher
- Derivative-Free and Blackbox Optimization
- Pattern Search Algorithms for Bound Constrained Minimization
- Derivative-free optimization methods
- Mesh Adaptive Direct Search Algorithms for Constrained Optimization
- A Simplex Method for Function Minimization
- Pyomo -- optimization modeling in Python
- Simulation optimization: a review of algorithms and applications
This page was built for publication: Data-driven spatial branch-and-bound algorithms for box-constrained simulation-based optimization
