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

    Solving the Traveling Telescope Problem with Mixed Integer Linear Programming

  • Velibor Mišić
    • Categories (Mixed) Integer Linear Programming, Applications - Science and Engineering, Integer Programming

    The size and complexity of modern astronomical surveys has grown to the point where, in many cases, traditional human scheduling of observations is tedious at best and impractical at worst. Automated scheduling algorithms present an opportunity to save human effort and increase scientific productivity. A common scheduling challenge involves determining the optimal ordering of a … Read more

    Optimizing the Path Towards Plastic-Free Oceans

  • Dick den Hertog
  • Jean Pauphilet
  • Yannick Pham
  • Bruno Sainte-Rose
  • Baizhi Song
    • Categories Applications - OR and Management Sciences, Dynamic Programming, Transportation Tags , , ,

    Increasing ocean plastic pollution is irreversibly harming ecosystems and human economic activities. We partner with a non-profit organization and use optimization to help clean up oceans from plastic faster. Specifically, we optimize the route of their plastic collection system in the ocean to maximize the quantity of plastic collected over time. We formulate the problem … Read more

    Sequential Pricing of Electricity

  • Jacob Mays
    • Categories Applications - OR and Management Sciences, Energy, Finance and Economics Tags , , ,

    This paper investigates the design and analysis of price formation in wholesale electricity markets given variability, uncertainty, non-convexity, and intertemporal operating constraints. The paper’s primary goal is to develop a framework to assess the many resource participation models, reserve product definitions, and enhanced pricing methods that have arisen in U.S. systems, especially in the context … Read more

    An enhanced mathematical model for optimal simultaneous preventive maintenance scheduling and workshop planning

  • Gabrijela Obradović
  • Ann-Brith Strömberg
    • Categories (Mixed) Integer Linear Programming, Multi-Criteria Optimization, Scheduling

    For a system to stay operational, maintenance of its components is required and to maximize the operational readiness of a system, preventive maintenance planning is essential. There are two stakeholders—a system operator and a maintenance workshop—and a contract regulating their joint activities. Each contract leads to a bi-objective optimization problem. Components that require maintenance are … Read more

    Column Generation in Column-and-Constraint Generation for Adjustable Robust Optimization with Interdiction-Type Linking Constraints

  • Henri Lefebvre
  • Martin Schmidt
  • Johannes Thürauf
    • Categories (Mixed) Integer Linear Programming, Robust Optimization Tags , , , ,

    Adjustable robust optimization (ARO) is a powerful tool to model problems that have uncertain data and that feature a two-stage decision-making process. Computationally, they are often addressed using the column-and-constraint generation (CCG) algorithm introduced by Zeng and Zhao (2013). While it was empirically shown that the algorithm scales well if all second-stage decisions are continuous, … Read more

    Continuous exact relaxation and alternating proximal gradient algorithm for partial sparse and partial group sparse optimization problems

  • Peng Dingtao
    • Categories Nonsmooth Optimization, Unconstrained Optimization Tags

    In this paper, we consider a partial sparse and partial group sparse optimization problem, where the loss function is a continuously differentiable function (possibly nonconvex), and the penalty term consists of two parts associated with sparsity and group sparsity. The first part is the $\ell_0$ norm of ${\bf x}$, the second part is the $\ell_{2,0}$ … Read more

    Zeroth-order Riemannian Averaging Stochastic Approximation Algorithms

  • Jiaxiang Li
  • Krishnakumar Balasubramanian
  • Shiqian Ma
    • Categories Constrained Nonlinear Optimization, Nonlinear Optimization, Stochastic Programming

    We present Zeroth-order Riemannian Averaging Stochastic Approximation (\texttt{Zo-RASA}) algorithms for stochastic optimization on Riemannian manifolds. We show that \texttt{Zo-RASA} achieves optimal sample complexities for generating $\epsilon$-approximation first-order stationary solutions using only one-sample or constant-order batches in each iteration. Our approach employs Riemannian moving-average stochastic gradient estimators, and a novel Riemannian-Lyapunov analysis technique for convergence analysis. … Read more

    On Tractable Convex Relaxations of Standard Quadratic Optimization Problems under Sparsity Constraints

  • Immanuel M. Bomze
  • Bo Peng
  • Yuzhou Qiu
  • E. Alper Yildirim
    • Categories (Mixed) Integer Nonlinear Programming, Quadratic Programming, Semi-definite Programming Tags , , , ,

    Standard quadratic optimization problems (StQPs) provide a versatile modelling tool in various applications. In this paper, we consider StQPs with a hard sparsity constraint, referred to as sparse StQPs. We focus on various tractable convex relaxations of sparse StQPs arising from a mixed-binary quadratic formulation, namely, the linear optimization relaxation given by the reformulation-linearization technique, … Read more

    Neur2RO: Neural Two-Stage Robust Optimization

  • Justin Dumouchelle
  • Esther Julien
  • Jannis Kurtz
  • Elias B. Khalil
    • Categories Robust Optimization Tags , ,

    Robust optimization provides a mathematical framework for modeling and solving decision-making problems under worst-case uncertainty. This work addresses two-stage robust optimization (2RO) problems (also called adjustable robust optimization), wherein first-stage and second-stage decisions are made before and after uncertainty is realized, respectively. This results in a nested min-max-min optimization problem which is extremely challenging computationally, … Read more