Ryan Cory-Wright

Evolving Scientific Discovery by Unifying Data and Background Knowledge with AI Hilbert

  • Ryan Cory-Wright
  • Cristina Cornelio
  • Sanjeeb Dash
  • Bachir El Khadir
  • Lior Horesh
    • Categories Basic Sciences Applications, Global Optimization Applications, Semi-definite Programming Tags , , ,

    The discovery of scientific formulae that parsimoniously explain natural phenomena and align with existing background theory is a key goal in science. Historically, scientists have derived natural laws by manipulating equations based on existing knowledge, forming new equations, and verifying them experimentally. In recent years, data-driven scientific discovery has emerged as a viable competitor in … Read more

    Gain Confidence, Reduce Disappointment: A New Approach to Cross-Validation for Sparse Regression

  • Ryan Cory-Wright
  • Andrés Gómez
    • Categories (Mixed) Integer Nonlinear Programming, Data Science Algorithms

    Ridge regularized sparse linear regression involves selecting a subset of features that explains the relationship between a high-dimensional design matrix and an output vector in an interpretable manner. To select the sparsity and robustness of linear regressors, techniques like leave-one-out cross-validation are commonly used for hyperparameter tuning. However, cross-validation typically increases the cost of sparse … Read more

    Optimal Low-Rank Matrix Completion: Semidefinite Relaxations and Eigenvector Disjunctions

  • Dimitris Bertsimas
  • Ryan Cory-Wright
  • Sean Lo
  • Jean Pauphilet
    • Categories Data-Mining, Global Optimization Theory, Semi-definite Programming

    Low-rank matrix completion consists of computing a matrix of minimal complexity that recovers a given set of observations as accurately as possible. Unfortunately, existing methods for matrix completion are heuristics that, while highly scalable and often identifying high-quality solutions, do not possess any optimality guarantees. We reexamine matrix completion with an optimality-oriented eye. We reformulate … Read more

    A Stochastic Benders Decomposition Scheme for Large-Scale Stochastic Network Design

  • Dimitris Bertsimas
  • Ryan Cory-Wright
  • Jean Pauphilet
  • Periklis Petridis
    • Categories Cutting Plane Approaches, Network Optimization, Stochastic Programming Tags , ,

    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 where operational costs are uncertain due to fluctuating demand and estimated as a sample average from historical data. This problem is computationally challenging, and instances with as few as  100 … Read more

    Sparse PCA With Multiple Components

  • Ryan Cory-Wright
  • Jean Pauphilet
    • Categories Data Science Theory, Data-Mining, Semi-definite Programming

    Sparse Principal Component Analysis (sPCA) is a cardinal technique for obtaining combinations of features, or principal components (PCs), that explain the variance of high-dimensional datasets in an interpretable manner. This involves solving a sparsity and orthogonality-constrained convex maximization problem, which is extremely computationally challenging. Most existing works address sparse PCA via methods—such as iteratively computing … Read more

    Decarbonizing OCP

  • Dimitris Bertsimas
  • Ryan Cory-Wright
  • Vassilis Digalakis Jr
    • Categories Applications - OR and Management Sciences, Production and Logistics, Robust Optimization

    Problem definition: We present our collaboration with the OCP Group, one of the world’s largest producers of phosphate and phosphate-based products, in support of a green initiative designed to reduce OCP’s carbon emissions significantly. We study the problem of decarbonizing OCP’s electricity supply by installing a mixture of solar panels and batteries to minimize its … Read more