On the Out-of-Sample Performance of Stochastic Dynamic Programming and Model Predictive Control

  • Dominic S. T. Keehan
  • Andrew B. Philpott
  • Edward Anderson
    • Categories Dynamic Programming, Stochastic Programming Tags , , ,

    Sample average approximation–based stochastic dynamic programming (SDP) and model predictive control (MPC) are two different methods for approaching multistage stochastic optimization. In this paper we investigate the conditions under which SDP may be outperformed by MPC. We show that, depending on the presence of concavity or convexity, MPC can be interpreted as solving a mean-constrained … Read more

    A Unified Approach for Maximizing Continuous $\gamma$-weakly DR-submodular Functions

  • Mohammad Pedramfar
  • Christopher Quinn
  • Vaneet Aggarwal
    • Categories Constrained Nonlinear Optimization, Stochastic Programming Tags , ,

    This paper presents a unified approach for maximizing continuous \(\gamma\)-weakly DR-submodular functions that encompasses a range of settings and oracle access types. Our approach includes a Frank-Wolfe type offline algorithm for both monotone and non-monotone functions, with different restrictions on the convex feasible region. We consider settings where the oracle provides access to either the … Read more

    Neur2BiLO: Neural Bilevel Optimization

  • Justin Dumouchelle
  • Esther Julien
  • Jannis Kurtz
  • Elias B. Khalil
    • Categories (Mixed) Integer Linear Programming, (Mixed) Integer Nonlinear Programming Tags , ,

    Bilevel optimization deals with nested problems in which a leader takes the first decision to minimize their objective function while accounting for a follower best-response reaction. Constrained bilevel problems with integer variables are particularly notorious for their hardness.  While exact solvers have been proposed for mixed-integer~linear bilevel optimization, they tend to scale poorly with problem … Read more

    ε-Optimality in Reverse Optimization

  • Mounir El Maghri
    • Categories Convex and Nonsmooth Optimization, Global Optimization Tags , , , ,

    The purpose of this paper is to completely characterize the global approximate optimality (ε-optimality) in reverse convex optimization under the general nonconvex constraint “h(x) ≥ 0″. The main condition presented is obtained in terms of Fenchel’s ε-subdifferentials thanks to El Maghri’s ε-efficiency in difference vector optimization [J. Glob. Optim. 61 (2015) 803–812], after converting the … Read more

    Managing Distributional Ambiguity in Stochastic Optimization through a Statistical Upper Bound Framework

  • Shixin Liu
  • Jian Hu
    • Categories Robust Optimization, Stochastic Programming

    Stochastic optimization is often hampered by distributional ambiguity, where critical probability distributions are poorly characterized or unknown. Addressing this challenge, we introduce a new framework that targets the minimization of a statistical upper bound for the expected value of uncertain objectives, facilitating more statistically robust decision-making. Central to our approach is the Average Percentile Upper … Read more

    Black-box optimization for the design of a jet plate for impingement cooling

  • Filippo Marini
  • Margherita Porcelli
  • Elisa Riccietti
  • Lorenzo Cocchi
    • Categories Applications - Science and Engineering, Optimization of Simulated Systems, Other Topics Tags , , ,

    In this work, we propose a novel black-box formulation of the impingement cooling system for a nozzle in a gas turbine. Leveraging on a well-known model that correlates the design features of the cooling system with the efficiency parameters, we develop NOZZLE, a new constrained black-box optimization formulation for the jet impingement cooling design. Then … Read more

    The stochastic Ravine accelerated gradient method with general extrapolation coefficients

  • Hédy Attouch
  • Jalal Fadili
  • Vyacheslav Kungurtsev
    • Categories Convex Optimization, Nonlinear Optimization Tags ,

    Abstract: In a real Hilbert space domain setting, we study the convergence properties of the stochastic Ravine accelerated gradient method for convex differentiable optimization. We consider the general form of this algorithm where the extrapolation coefficients can vary with each iteration, and where the evaluation of the gradient is subject to random errors. This general … Read more

    A Jacobi-type Newton method for Nash equilibrium problems with descent guarantees

  • Oliver Kolossoski
  • Luís Felipe Bueno
  • Gabriel Haeser
    • Categories Game Theory, Nonlinear Optimization, Other Topics Tags , , , ,

    A common strategy for solving an unconstrained two-player Nash equilibrium problem with continuous variables is applying Newton’s method to the system obtained by the corresponding first-order necessary optimality conditions. However, when taking into account the game dynamics, it is not clear what is the goal of each player when considering they are taking their current … Read more

    Data Collaboration Analysis with Orthonormal Basis Selection and Alignment

  • Keiyu Nosaka
  • Yuichi Takano
  • Akiko Yoshise
    • Categories Linear, Cone and Semidefinite Programming, Optimization in Data Science, Other Topics

    Data Collaboration (DC) enables multiple parties to jointly train a model by sharing only linear projections of their private datasets. The core challenge in DC is to align the bases of these projections without revealing each party’s secret basis. While existing theory suggests that any target basis spanning the common subspace should suffice, in practice, … Read more

    A Polyhedral Characterization of Linearizable Quadratic Combinatorial Optimization Problems

  • Lucas Waddell
    • Categories 0-1 Programming, Combinatorial Optimization, Quadratic Programming Tags , , ,

    We introduce a polyhedral framework for characterizing instances of quadratic combinatorial optimization programs (QCOPs) as being linearizable, meaning that the quadratic objective can be equivalently rewritten as linear in such a manner that preserves the objective function value at all feasible solutions. In particular, we show that an instance is linearizable if and only if … Read more