Other Topics

Global convergence of a BFGS-type algorithm for nonconvex multiobjective optimization problems

  • Leandro Fonseca Prudente
  • D. R. Souza
    • Categories Multi-Criteria Optimization Tags , , , , , ,

    We propose a modified BFGS algorithm for multiobjective optimization problems with global convergence, even in the absence of convexity assumptions on the objective functions. Furthermore, we establish the superlinear convergence of the method under usual conditions. Our approach employs Wolfe step sizes and ensures that the Hessian approximations are updated and corrected at each iteration … Read more

    Diagonal Partitioning Strategy Using Bisection of Rectangles and a Novel Sampling Scheme

  • Lakhdar Chiter
    • Categories Global Optimization Applications, Other Topics Tags

    In this paper we consider a global optimization problem, where the objective function is supposed to be Lipschitz-continuous with an unknown Lipschitz constant. Based on the recently introduced BIRECT (BIsection of RECTangles) algorithm, a new diagonal partitioning and sampling scheme is introduced. 0ur framework, called BIRECT-V (where V stands for vertices), combines bisection with sampling … Read more

    A novel UCB-based batch strategy for Bayesian optimization

  • Carmen-Ana Domínguez-Bravo
    • Categories Global Optimization, Multi-Criteria Optimization Tags , , ,

    The optimization of expensive black-box functions appears in many situations. Bayesian optimization methods have been successfully applied to solve these prob- lems using well-known single-point acquisition functions. Nowadays, the develop- ments in technology allow us to perform evaluations of some of these expensive function in parallel. Therefore, there is a need for batch infill criteria … Read more

    Playing Stackelberg security games in perfect formulations

  • Victor Bucarey
  • Pamela Bustamante-Faúndez
  • Vladimir
  • Martine Labbé
  • Fernando Ordóñez
    • Categories Applications - OR and Management Sciences, Game Theory, Network Optimization

    Protecting critical infrastructure from intentional damage requires foreseeing the strategies of possible attackers. The problem faced by the defender of such infrastructure can be formulated as a Stackelberg security game. A defender must decide what specific targets to protect with limited resources, maximizing their expected utility (e.g., minimizing damage value) and considering that a second … Read more

    Sensitivity-based decision support for critical measures using the example of COVID-19 dynamics

  • Falk M. Hante
  • Shanshan Meng
    • Categories Applications - OR and Management Sciences, Optimization of Simulated Systems, Other Topics Tags , , , ,

    We parametrize public policies in the context of the COVID-19 pandemic to evaluate the effectiveness of policies through sensitivity-based methods in order to offer insights into understanding the contributions to critical measures in retrospective. The study utilizes a group-specific SEIR model with a tracing and isolation strategy and vaccination programs. Public policies are applied to … Read more

    Using dual relaxations in multiobjective mixed-integer quadratic programming

  • Marianna De Santis
  • Gabriele Eichfelder
  • Daniele Patria
  • Leo Warnow
    • Categories (Mixed) Integer Nonlinear Programming, Multi-Criteria Optimization Tags , , ,

    We present a branch-and-bound method for multiobjective mixed-integer convex quadratic programs that computes a superset of efficient integer assignments and a coverage of the nondominated set. The method relies on outer approximations of the upper image set of continuous relaxations. These outer approximations are obtained addressing the dual formulations of specific subproblems where the values … Read more

    Distributionally Robust Linear Quadratic Control

  • Bahar Tașkesen
  • Dan Iancu
  • Çağıl Koçyiğit
  • Daniel Kuhn
    • Categories Control Applications, Dynamic Programming, Robust Optimization

    Linear-Quadratic-Gaussian (LQG) control is a fundamental control paradigm that is studied in various fields such as engineering, computer science, economics, and neuroscience. It involves controlling a system with linear dynamics and imperfect observations, subject to additive noise, with the goal of minimizing a quadratic cost function for the state and control variables. In this work, … Read more

    The complexity of first-order optimization methods from a metric perspective

  • Adrian Lewis
  • Tonghua Tian
    • Categories Nonsmooth Optimization, Other Topics Tags , , , , ,

    A central tool for understanding first-order optimization algorithms is the Kurdyka-Lojasiewicz inequality. Standard approaches to such methods rely crucially on this inequality to leverage sufficient decrease conditions involving gradients or subgradients. However, the KL property fundamentally concerns not subgradients but rather “slope”, a purely metric notion. By highlighting this view, and avoiding any use of … Read more

    Numerical Methods for Convex Multistage Stochastic Optimization

  • Guanghui Lan
  • Alexander Shapiro
    • Categories Convex Optimization, Dynamic Programming, Stochastic Programming Tags , , , , , , ,

    Optimization problems involving sequential decisions in  a  stochastic environment    were studied  in  Stochastic Programming (SP), Stochastic Optimal Control  (SOC) and Markov Decision Processes (MDP). In this paper we mainly concentrate on SP and  SOC modelling   approaches. In these frameworks there are natural situations  when the considered problems are  convex. Classical approach to sequential optimization … Read more

    Hidden convexity, optimization, and algorithms on rotation matrices

  • Kevin Shu
  • Alex L. Wang
  • Akshay Ramachandran
    • Categories Constrained Nonlinear Optimization, Other Topics, Semi-definite Programming Tags , ,

    This paper studies hidden convexity properties associated with constrained optimization problems over the set of rotation matrices \(\text{SO}(n)\). Such problems are nonconvex due to the constraint\(X\in\text{SO}(n)\). Nonetheless, we show that certain linear images of \(\text{SO}(n)\) are convex, opening up the possibility for convex optimization algorithms with provable guarantees for these problems. Our main technical contributions … Read more