Behavior of Newton-type methods near critical solutions of nonlinear equations with semismooth derivatives

  • Andreas Fischer
  • Alexey F. Izmailov
  • Mario Jelitte
    • Categories Nonlinear Optimization, Nonsmooth Optimization Tags , ,

    Having in mind singular solutions of smooth reformulations of complementarity problems, arising unavoidably when the solution in question violates strict complementarity, we study the behavior of Newton-type methods near singular solutions of nonlinear equations, assuming that the operator of the equation possesses a strongly semismooth derivative, but is not necessarily twice differentiable. These smoothness restrictions … Read more

    Revisiting Capacity Market Fundamentals

  • Ke Xin Zuo
  • Jacob Mays
    • Categories Energy, Finance and Economics Tags , , , ,

    Many liberalized electricity markets use capacity mechanisms to ensure that sufficient resources will be available in advance of operations. Recent events have called into question the ability of capacity mechanisms to provide sufficient incentives for reliability. A core challenge is that penalties for non-performance on capacity obligations are lower than what theory would suggest is … Read more

    Mixed-Integer Programming for a Class of Robust Submodular Maximization Problems

  • Hsin-Yi Huang
  • Hao-Hsiang Wu
  • Simge Küçükyavuz
    • Categories (Mixed) Integer Linear Programming, Robust Optimization, Stochastic Programming

    We consider robust submodular maximization problems (RSMs), where given a set of \(m\) monotone submodular objective functions, the robustness is with respect to the worst-case (scaled) objective function. The model we consider generalizes two variants of robust submodular maximization problems in the literature, depending on the choice of the scaling vector. On one hand, by using … Read more

    Searching for Optimal Per-Coordinate Step-sizes with Multidimensional Backtracking

  • Frederik Kunstner
  • Mark Schmidt
    • Categories Convex Optimization, Nonlinear Optimization Tags , ,

    The backtracking line-search is an effective technique to automatically tune the step-size in smooth optimization. It guarantees similar performance to using the theoretically optimal step-size. Many approaches have been developed to instead tune per-coordinate step-sizes, also known as diagonal preconditioners, but none of the existing methods are provably competitive with the optimal per-coordinate stepsizes. We … Read more

    End-to-End Learning for Stochastic Optimization: A Bayesian Perspective

  • Yves Rychener
  • Daniel Kuhn
  • Tobias Sutter
    • Categories Stochastic Programming Tags , , ,

    We develop a principled approach to end-to-end learning in stochastic optimization. First, we show that the standard end-to-end learning algorithm admits a Bayesian interpretation and trains a posterior Bayes action map. Building on the insights of this analysis, we then propose new end-to-end learning algorithms for training decision maps that output solutions of empirical risk … Read more

    Nonlinear Distributionally Robust Optimization

  • Rayyan
  • Peyman Mohajerin Esfahani
    • Categories Infinite Dimensional Optimization, Optimization in Data Science, Robust Optimization Tags , ,

    This article focuses on a class of distributionally robust optimization (DRO) problems where, unlike the growing body of the literature, the objective function is potentially non-linear in the distribution. Existing methods to optimize nonlinear functions in probability space use the Frechet derivatives, which present both theoretical and computational challenges. Motivated by this, we propose an … Read more

    Compressed Sensing: A Discrete Optimization Approach

  • Dimitris Bertsimas
  • Nicholas A. G. Johnson
    • Categories (Mixed) Integer Nonlinear Programming, Second-Order Cone Programming, Statistics Tags , , , ,

    We study the Compressed Sensing (CS) problem, which is the problem of finding the most sparse vector that satisfies a set of linear measurements up to some numerical tolerance. CS is a central problem in Statistics, Operations Research and Machine Learning which arises in applications such as signal processing, data compression, image reconstruction, and multi-label … Read more

    Outlier detection in regression: conic quadratic formulations

  • Andrés Gómez
  • José Neto
    • Categories (Mixed) Integer Nonlinear Programming, Statistics Tags , , ,

    In many applications, when building linear regression models, it is important to account for the presence of outliers, i.e., corrupted input data points. Such problems can be formulated as mixed-integer optimization problems involving cubic terms, each given by the product of a binary variable and a quadratic term of the continuous variables. Existing approaches in … Read more

    New Formulations and Pricing Mechanisms for Stochastic Electricity Market Clearing Problem

  • Saumya Sakitha Sashrika Ariyarathne
  • Harsha Gangammanavar
    • Categories Applications - OR and Management Sciences, Energy Tags , ,

    We present new formulations of the stochastic electricity market clearing problem based on the principles of stochastic programming. Previous analyses have established that the canonical stochastic programming model effectively captures the relationship between the day-ahead and real-time dispatch and prices. The resulting quantities exhibit desirable guarantees of revenue adequacy, cost recovery, and price distortion in … Read more