Optimization in Data Science

Mean–variance portfolio optimization with shrinkage estimation for recommender systems

  • Yuichi Takano
    • Categories Integer Programming, Optimization in Data Science Tags , , , ,

    This paper is concerned with a mean-variance portfolio optimization model with cardinality constraint for generating high-quality lists of recommendations. It is usually difficult to accurately estimate the rating covariance matrix required for mean-variance portfolio optimization because of a shortage of observed user ratings. To improve the accuracy of covariance matrix estimation, we apply shrinkage estimation … Read more

    Analysis of a Class of Minimization Problems Lacking Lower Semicontinuity

  • Shaoning Han
  • Ying Cui
  • Jong-Shi Pang
    • Categories Nonlinear Optimization, Nonsmooth Optimization, Optimization in Data Science Tags , , ,

    The minimization of non-lower semicontinuous functions is a difficult topic that has been minimally studied. Among such functions is a Heaviside composite function that is the composition of a Heaviside function with a possibly nonsmooth multivariate function. Unifying a statistical estimation problem with hierarchical selection of variables and a sample average approximation of composite chance … Read more

    An Integer Programming Approach To Subspace Clustering With Missing Data

  • Akhilesh Soni
  • Jeff Linderoth
  • James R. Luedtke
  • Daniel Pimentel-Alarcon
    • Categories Data Science Algorithms, Integer Programming Tags , ,

    In the Subspace Clustering with Missing Data (SCMD) problem, we are given a collection of n partially observed d-dimensional vectors. The data points are assumed to be concentrated near a union of low-dimensional subspaces. The goal of SCMD is to cluster the vectors according to their subspace membership and recover the underlying basis, which can … Read more

    Conjecturing-Based Discovery of Patterns in Data

  • J. Paul Brooks
  • David Edwards
  • C. E. Larson
  • Nico Van Cleemput
    • Categories Applications - OR and Management Sciences, Data Science Algorithms, Optimization in Data Science Tags , , , ,

    We propose the use of a conjecturing machine that suggests feature relationships in the form of bounds involving nonlinear terms for numerical features and boolean expressions for categorical features. The proposed Conjecturing framework recovers known nonlinear and boolean relationships among features from data. In both settings, true underlying relationships are revealed. We then compare the … Read more

    Learning the Follower’s Objective Function in Sequential Bilevel Games

  • Ioana Molan
  • Martin Schmidt
  • Johannes Thürauf
    • Categories Data Science Algorithms, Other Topics

    We consider bilevel optimization problems in which the leader has no or only partial knowledge about the objective function of the follower. The studied setting is a sequential one in which the bilevel game is played repeatedly. This allows the leader to learn the objective function (values) of the follower over time. We focus on … Read more

    Data-Driven Counterfactual Optimization For Personalized Clinical Decision-Making

  • Che-Yi Liao
  • Esmaeil Keyvanshokooh
  • Gian-Gabriel Garcia
    • Categories Applications - OR and Management Sciences, Optimization in Data Science Tags , , , ,

    Chronic diseases have a significant impact on global mortality rates and healthcare costs. Notably, machine learning-based clinical assessment tools are becoming increasingly popular for informing treatment targets for high-risk patients with chronic diseases. However, using these tools alone, it is challenging to identify personalized treatment targets that lower the risks of adverse outcomes to a … Read more

    Almost-sure convergence of iterates and multipliers in stochastic sequential quadratic optimization

  • Frank E. Curtis
  • Xin Jiang
  • Qi Wang
    • Categories Constrained Nonlinear Optimization, Data Science Applications, Stochastic Programming Tags , , ,

    Stochastic sequential quadratic optimization (SQP) methods for solving continuous optimization problems with nonlinear equality constraints have attracted attention recently, such as for solving large-scale data-fitting problems subject to nonconvex constraints. However, for a recently proposed subclass of such methods that is built on the popular stochastic-gradient methodology from the unconstrained setting, convergence guarantees have been … Read more

    Solution Path of Time-varying Markov Random Fields with Discrete Regularization

  • Salar Fattahi
  • Andrés Gómez
    • Categories Dynamic Programming, Integer Programming, Optimization in Data Science

    We study the problem of inferring sparse time-varying Markov random fields (MRFs) with different discrete and temporal regularizations on the parameters. Due to the intractability of discrete regularization, most approaches for solving this problem rely on the so-called maximum-likelihood estimation (MLE) with relaxed regularization, which neither results in ideal statistical properties nor scale to the … Read more

    Inexact Direct-Search Methods for Bilevel Optimization Problems

  • Youssef Diouane
  • Vyacheslav Kungurtsev
  • Francesco Rinaldi
  • Damiano Zeffiro
    • Categories Nonlinear Optimization, Nonsmooth Optimization, Optimization in Data Science

    In this work, we introduce new direct search schemes for the solution of bilevel optimization (BO) problems. Our methods rely on a fixed accuracy black box oracle for the lower-level problem, and deal both with smooth and potentially nonsmooth true objectives. We thus analyze for the first time in the literature direct search schemes in … Read more

    Structured Pruning of Neural Networks for Constraints Learning

  • Matteo Cacciola
  • Andrea Lodi
  • Antonio Frangioni
    • Categories (Mixed) Integer Linear Programming, Combinatorial Optimization, Optimization in Data Science Tags , ,

    In recent years, the integration of Machine Learning (ML) models with Operation Research (OR) tools has gained popularity across diverse applications, including cancer treatment, algorithmic configuration, and chemical process optimization. In this domain, the combination of ML and OR often relies on representing the ML model output using Mixed Integer Programming (MIP) formulations. Numerous studies … Read more