“There are some things which cannot be learned quickly, and time, which is all we have, must be paid heavily for their acquiring. They are the very simplest things, and because it takes a man’s life to know them the little new that each man gets from life is very costly and the only heritage he has to leave.” - Ernest Hemingway (More…)
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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.