60 citations · 197 across the 15 of their papers we have counts for
8 papers · 1 filter
Improved Approximation Algorithms for Orthogonally Constrained Problems Using Semidefinite Optimization
Ryan Cory-Wright, Jean Pauphilet
Building on the blueprint from Goemans and Williamson (1995) for the Max-Cut problem, we construct a polynomial-time approximation algorithm for orthogonally constrained quadratic…
A Stochastic Benders Decomposition Scheme for Large-Scale Stochastic Network Design
Dimitris Bertsimas, Ryan Cory-Wright, Jean Pauphilet +1
Network design problems involve constructing edges in a transportation or supply chain network to minimize construction and daily operational costs. We study a stochastic version w…
Sparse PCA With Multiple Components
Ryan Cory-Wright, Jean Pauphilet
Sparse Principal Component Analysis (sPCA) is a cardinal technique for obtaining combinations of features, or principal components (PCs), that explain the variance of high-dimensio…
A new perspective on low-rank optimization
Dimitris Bertsimas, Ryan Cory-Wright, Jean Pauphilet
A key question in many low-rank problems throughout optimization, machine learning, and statistics is to characterize the convex hulls of simple low-rank sets and judiciously apply…
Mixed-Projection Conic Optimization: A New Paradigm for Modeling Rank Constraints
Dimitris Bertsimas, Ryan Cory-Wright, Jean Pauphilet
We propose a framework for modeling and solving low-rank optimization problems to certifiable optimality. We introduce symmetric projection matrices that satisfy , the matri…
Solving Large-Scale Sparse PCA to Certifiable (Near) Optimality
Dimitris Bertsimas, Ryan Cory-Wright, Jean Pauphilet
Sparse principal component analysis (PCA) is a popular dimensionality reduction technique for obtaining principal components which are linear combinations of a small subset of the…
A unified approach to mixed-integer optimization problems with logical constraints
Dimitris Bertsimas, Ryan Cory-Wright, Jean Pauphilet
We propose a unified framework to address a family of classical mixed-integer optimization problems with logically constrained decision variables, including network design, facilit…
Sparse Classification: a scalable discrete optimization perspective
Dimitris Bertsimas, Jean Pauphilet, Bart Van Parys
We formulate the sparse classification problem of samples with features as a binary convex optimization problem and propose a cutting-plane algorithm to solve it exactly. F…