Convex Optimization ⁷

Optimization Papers in JMLR Volume 26

Optimization Research Papers in JMLR Volume 25

Optimization Research Papers in JMLR Volume 25 (2024) # This document lists papers from JMLR Volume 25 (2024) that focus on optimization research, categorized by their primary themes. Each paper is numbered starting from 1 within its subsection, with a brief description of its key contributions to optimization theory, algorithms, or applications. Convex Optimization # Papers addressing convex optimization problems, including sparse NMF, differential privacy, and sparse regression. Lower Complexity Bounds of Finite-Sum Optimization Problems: The Results and Construction Authors: Yuze Han, Guangzeng Xie, Zhihua Zhang Description: Investigates lower complexity bounds for finite-sum optimization problems in convex settings.

Mathematics - Optimization

Branches of Optimization Research # Convex Optimization # Convex optimization focuses on problems where the objective function and constraints are convex, ensuring a single global optimum. This field is foundational in machine learning, signal processing, and control systems due to its guaranteed convergence and efficient algorithms. Convex Optimization by Boyd and Vandenberghe - PDF Convex Optimization Theory by Dimitri P. Bertsekas - PDF Discrete, Combinatorial, and Integer Optimization # This branch deals with optimization problems involving discrete variables, such as integers or combinatorial structures, often encountered in scheduling, network design, and logistics. Bayesian optimization, a subset, is particularly useful for optimizing expensive black-box functions.

Optimization Research Papers in JMLR Volume 24

Optimization Research Papers in JMLR Volume 24 (2023) # This document lists papers from JMLR Volume 24 (2023) that focus on optimization research, categorized by their primary themes. Each paper is numbered starting from 1 within its subsection, with a brief description of its key contributions to optimization theory, algorithms, or applications. Convex Optimization # Papers addressing convex optimization problems, including sparse PCA, L0 regularization, and matrix decomposition. Sparse PCA: A Geometric Approach Authors: Dimitris Bertsimas, Driss Lahlou Kitane Description: Develops a geometric approach for sparse principal component analysis using convex optimization techniques.

Optimization Research Papers in JMLR Volume 23

Optimization Research Papers in JMLR Volume 23 (2022) # This document lists papers from JMLR Volume 23 (2022) that focus on optimization research, categorized by their primary themes. Each paper is numbered starting from 1 within its subsection, with a brief description of its key contributions to optimization theory, algorithms, or applications. Convex Optimization # Papers addressing convex optimization problems, including sparse PCA, L1-regularized SVMs, and metric-constrained problems. Solving Large-Scale Sparse PCA to Certifiable (Near) Optimality Authors: Dimitris Bertsimas, Ryan Cory-Wright, Jean Pauphilet Description: Develops convex optimization techniques for large-scale sparse principal component analysis with certifiable near-optimal solutions.

Optimization Research Papers in JMLR Volume 22

Optimization Research Papers in JMLR Volume 22 (2021) # This document lists papers from JMLR Volume 22 (2021) that focus on optimization research, categorized by their primary themes. Each paper is numbered starting from 1 within its subsection, with a brief description of its key contributions to optimization theory, algorithms, or applications. Convex Optimization # Papers addressing convex optimization problems, including clustering, Wasserstein barycenters, sparse optimization, and bandits. Convex Clustering: Model, Theoretical Guarantee and Efficient Algorithm Authors: Defeng Sun, Kim-Chuan Toh, Yancheng Yuan Description: Proposes a convex clustering model with theoretical guarantees and an efficient algorithm.

Optimization Research Papers in JMLR Volume 21

Optimization Research Papers in JMLR Volume 21 (2020) # This document lists papers from JMLR Volume 21 (2020) that focus on optimization research, categorized by their primary themes. Each paper is numbered starting from 1 within its subsection, with a brief description of its key contributions to optimization theory, algorithms, or applications. Convex Optimization # Papers addressing convex optimization problems, including complexity bounds, convergence analysis, and applications in regression and assortment optimization.