Quantifying uncertainty with ensembles of surrogates for blackbox optimization
From MaRDI portal
Publication:2162525
DOI10.1007/s10589-022-00381-zzbMath1496.90091arXiv2107.04360OpenAlexW3180164923MaRDI QIDQ2162525
Renaud Saltet, Charles Audet, Sébastien Le Digabel
Publication date: 8 August 2022
Published in: Computational Optimization and Applications (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2107.04360
derivative-free optimizationmesh adaptive direct searchblackbox optimizationBayesian optimizationensembles of surrogates
Nonlinear programming (90C30) Derivative-free methods and methods using generalized derivatives (90C56)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Constrained global optimization of expensive black box functions using radial basis functions
- Stochastic radial basis function algorithms for large-scale optimization involving expensive black-box objective and constraint functions
- Mixture surrogate models based on Dempster-Shafer theory for global optimization problems
- Locally weighted regression models for surrogate-assisted design optimization
- Spent potliner treatment process optimization using a MADS algorithm
- Nonsmooth optimization through mesh adaptive direct search and variable neighborhood search
- Incorporating minimum Frobenius norm models in direct search
- Test examples for nonlinear programming codes
- Efficient global optimization of expensive black-box functions
- Optimization of automotive valve train components with implicit filtering
- Design and analysis of computer experiments. With comments and a rejoinder by the authors
- On the construction of quadratic models for derivative-free trust-region algorithms
- Order-based error for managing ensembles of surrogates in mesh adaptive direct search
- A taxonomy of global optimization methods based on response surfaces
- Global optimization of costly nonconvex functions using radial basis functions
- Optimizing radial basis functions by d.c. programming and its use in direct search for global derivative-free optimization
- Surrogate optimization of deep neural networks for groundwater predictions
- Dynamic improvements of static surrogates in direct search optimization
- Efficient global optimization algorithm assisted by multiple surrogate techniques
- Sequential approximate optimization using radial basis function network for engineering optimization
- Convergence results for generalized pattern search algorithms are tight
- A Linesearch-Based Derivative-Free Approach for Nonsmooth Constrained Optimization
- A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code
- Global Convergence of Radial Basis Function Trust Region Derivative-Free Algorithms
- On the Convergence of Pattern Search Algorithms
- Parallel Space Decomposition of the Mesh Adaptive Direct Search Algorithm
- Trailing-edge noise reduction using derivative-free optimization and large-eddy simulation
- OrthoMADS: A Deterministic MADS Instance with Orthogonal Directions
- Introduction to Derivative-Free Optimization
- ORBIT: Optimization by Radial Basis Function Interpolation in Trust-Regions
- Optimization and nonsmooth analysis
- Derivative-Free and Blackbox Optimization
- Detection and Remediation of Stagnation in the Nelder--Mead Algorithm Using a Sufficient Decrease Condition
- Use of quadratic models with mesh-adaptive direct search for constrained black box optimization
- Surrogate Optimization of Computationally Expensive Black-Box Problems with Hidden Constraints
- Benchmarking Derivative-Free Optimization Algorithms
- A Progressive Barrier for Derivative-Free Nonlinear Programming
- Derivative-free optimization methods
- Dynamic Trees for Learning and Design
- Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting
- Surrogate‐based methods for black‐box optimization
- Mesh Adaptive Direct Search Algorithms for Constrained Optimization
- Finding Optimal Algorithmic Parameters Using Derivative‐Free Optimization
- Theory of Positive Linear Dependence
- An algorithmic framework for the optimization of computationally expensive bi-fidelity black-box problems
- Algorithm 1027: NOMAD Version 4: Nonlinear Optimization with the MADS Algorithm