Parallel distributed block coordinate descent methods based on pairwise comparison oracle
From MaRDI portal
Publication:1675626
DOI10.1007/s10898-016-0465-xzbMath1380.90258arXiv1409.3912OpenAlexW2963649887MaRDI QIDQ1675626
Takafumi Kanamori, Wataru Kumagai, Kota Matsui
Publication date: 2 November 2017
Published in: Journal of Global Optimization (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1409.3912
Nonlinear programming (90C30) Derivative-free methods and methods using generalized derivatives (90C56)
Related Items (3)
Information geometry approach to parameter estimation in Markov chains ⋮ A one-bit, comparison-based gradient estimator ⋮ Information geometry approach to parameter estimation in hidden Markov model
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Parallel coordinate descent methods for big data optimization
- Implementing the Nelder-Mead simplex algorithm with adaptive parameters
- The \(K\)-armed dueling bandits problem
- Linear and nonlinear programming.
- Derivative-free optimization: a review of algorithms and comparison of software implementations
- Algorithmic Connections between Active Learning and Stochastic Convex Optimization
- Reducing the Number of Function Evaluations in Mesh Adaptive Direct Search Algorithms
- Parallel Space Decomposition of the Mesh Adaptive Direct Search Algorithm
- Active Learning in the Non-realizable Case
- Using simplex gradients of nonsmooth functions in direct search methods
- Introduction to Derivative-Free Optimization
- Convergence Properties of the Nelder--Mead Simplex Method in Low Dimensions
- Analysis of Generalized Pattern Searches
- Use of quadratic models with mesh-adaptive direct search for constrained black box optimization
- A Simplex Method for Function Minimization
This page was built for publication: Parallel distributed block coordinate descent methods based on pairwise comparison oracle