Bound-constrained global optimization of functions with low effective dimensionality using multiple random embeddings
From MaRDI portal
Publication:2687068
DOI10.1007/s10107-022-01812-9OpenAlexW4280607149MaRDI QIDQ2687068
Adilet Otemissov, Coralia Cartis, Estelle M. Massart
Publication date: 1 March 2023
Published in: Mathematical Programming. Series A. Series B (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2009.10446
global optimizationconstrained optimizationrandom embeddingsdimensionality reduction techniquesfunctions with low effective dimensionality
Numerical mathematical programming methods (65K05) Nonconvex programming, global optimization (90C26) Nonlinear programming (90C30) Optimality conditions for problems involving randomness (49K45)
Related Items
Scalable subspace methods for derivative-free nonlinear least-squares optimization, Optimization on Manifolds via Graph Gaussian Processes, Global optimization using random embeddings
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Learning functions of few arbitrary linear parameters in high dimensions
- Learning non-parametric basis independent models from point queries via low-rank methods
- Lipschitzian optimization without the Lipschitz constant
- \(L_p\)-norm spherical distribution
- Global convergence rate analysis of unconstrained optimization methods based on probabilistic models
- A polyhedral branch-and-cut approach to global optimization
- A comparison of complete global optimization solvers
- Random projections for conic programs
- Random gradient-free minimization of convex functions
- Asymptotic Approximations of Integrals
- Bayesian Optimization in a Billion Dimensions via Random Embeddings
- Convergence of Trust-Region Methods Based on Probabilistic Models
- Active Subspaces
- Asymptotic Methods for Integrals
- Probability
- High-Dimensional Probability
- A dimensionality reduction technique for unconstrained global optimization of functions with low effective dimensionality
- Random Projections for Linear Programming
- Universality laws for randomized dimension reduction, with applications
- Approximations to the probability integral and certain percentage points of a multivariate analogue of Student's t-distribution
- Benchmarking optimization software with performance profiles.
- A locally-biased form of the DIRECT algorithm.
