714 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Massachusetts Institute of TechnologyUS7 papers
- Imperial College LondonGB3 papers
- Centre for Economic Policy ResearchGB2 papers
- Georgia Institute of TechnologyUS2 papers
- University of ExeterGB2 papers
- University of WarwickGB2 papers
- Aalto UniversityFI1 paper
- AGH University of KrakowPL1 paper
- Agriculture and FoodAU1 paper
- Atilim UniversityTR1 paper
- Bilkent UniversityTR1 paper
- Cardiff UniversityGB1 paper
7 papers · 1 filter
Operational Research: Methods and Applications
Fotios Petropoulos, Gilbert Laporte, Emel Aktas +79
Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This…
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…
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…