A tutorial on Bayesian optimization
PI Frazier - arxiv preprint arxiv:1807.02811, 2018 - arxiv.org
Bayesian optimization is an approach to optimizing objective functions that take a long time
(minutes or hours) to evaluate. It is best-suited for optimization over continuous domains of …
(minutes or hours) to evaluate. It is best-suited for optimization over continuous domains of …
Advances in surrogate based modeling, feasibility analysis, and optimization: A review
The idea of using a simpler surrogate to represent a complex phenomenon has gained
increasing popularity over past three decades. Due to their ability to exploit the black-box …
increasing popularity over past three decades. Due to their ability to exploit the black-box …
Bayesian optimization
PI Frazier - Recent advances in optimization and modeling …, 2018 - pubsonline.informs.org
Bayesian optimization is an approach to optimizing objective functions that take a long time
(minutes or hours) to evaluate. It is best suited for optimization over continuous domains of …
(minutes or hours) to evaluate. It is best suited for optimization over continuous domains of …
Derivative-free optimization: a review of algorithms and comparison of software implementations
This paper addresses the solution of bound-constrained optimization problems using
algorithms that require only the availability of objective function values but no derivative …
algorithms that require only the availability of objective function values but no derivative …
Review of surrogate modeling in water resources
Surrogate modeling, also called metamodeling, has evolved and been extensively used
over the past decades. A wide variety of methods and tools have been introduced for …
over the past decades. A wide variety of methods and tools have been introduced for …
[KNIHA][B] Surrogate-model-based design and optimization
Surrogate-Model-Based Design and Optimization | SpringerLink Skip to main content
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Advertisement SpringerLink Account Menu Find a journal Publish with us Track your research …
Global optimization advances in mixed-integer nonlinear programming, MINLP, and constrained derivative-free optimization, CDFO
This manuscript reviews recent advances in deterministic global optimization for Mixed-
Integer Nonlinear Programming (MINLP), as well as Constrained Derivative-Free …
Integer Nonlinear Programming (MINLP), as well as Constrained Derivative-Free …
A review of recent advances in global optimization
This paper presents an overview of the research progress in deterministic global
optimization during the last decade (1998–2008). It covers the areas of twice continuously …
optimization during the last decade (1998–2008). It covers the areas of twice continuously …
RBFOpt: an open-source library for black-box optimization with costly function evaluations
We consider the problem of optimizing an unknown function given as an oracle over a mixed-
integer box-constrained set. We assume that the oracle is expensive to evaluate, so that …
integer box-constrained set. We assume that the oracle is expensive to evaluate, so that …
Combining radial basis function surrogates and dynamic coordinate search in high-dimensional expensive black-box optimization
This article presents the DYCORS (DYnamic COordinate search using Response Surface
models) framework for surrogate-based optimization of HEB (High-dimensional, Expensive …
models) framework for surrogate-based optimization of HEB (High-dimensional, Expensive …