Worst-Case Conditional Value at Risk for Asset Liability Management: A Novel Framework for General Loss Functions

  • Alireza Ghahtarani
  • Ahmed Saif
  • Alireza Ghasemi
    • Categories Applications - OR and Management Sciences, Finance and Economics, Robust Optimization Tags , , , ,

    Asset-liability management (ALM) is a challenging task faced by pension funds due to the uncertain nature of future asset returns and interest rates. To address this challenge, this paper presents a new mathematical model that uses aWorst-case Conditional Value-at-Risk (WCVaR) constraint to ensure that the funding ratio remains above a regulator-mandated threshold with a high … Read more

    Optimized Dimensionality Reduction for Moment-based Distributionally Robust Optimization

  • ShiyiJiang
  • Jianqiang Cheng
  • Kai Pan
  • Z.J. Max Shen
    • Categories Optimization in Data Science, Robust Optimization, Semi-definite Programming Tags , , , ,

    Moment-based distributionally robust optimization (DRO) provides an optimization framework to integrate statistical information with traditional optimization approaches. Under this framework, one assumes that the underlying joint distribution of random parameters runs in a distributional ambiguity set constructed by moment information and makes decisions against the worst-case distribution within the set. Although most moment-based DRO problems … 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

    Water resources management: A bibliometric analysis and future research directions

  • Frédéric Babonneau
    • Categories Applications - OR and Management Sciences, Stochastic Programming Tags , , , ,

    The stochastic dual dynamic programming (SDDP) algorithm introduced by Pereira and Pinto in 1991 has sparked essential research in the context of water resources management, mainly due to its ability to address large-scale multistage stochastic problems. This paper aims to provide a tutorial-type review of 32 years of research since the publication of the SDDP … Read more

    Fair stochastic vehicle routing with partial deliveries

  • Natasja Sluijk
  • Walter Rei
  • Joris Kinable
  • Michel Gendreau
  • Tom Van Woensel
    • Categories Branch and Cut Algorithms, Stochastic Programming, Transportation Tags , , ,

    A common assumption in the models for the vehicle routing problem with stochastic demands is that all demands must be satisfied. This is achieved by including recourse actions in two-stage stochastic programming formulations or by ensuring with a high probability that all demand fits within the vehicle capacity (chance-constrained formulations). In this work, we relax … Read more

    When Deep Learning Meets Polyhedral Theory: A Survey

  • Joey Huchette
  • Gonzalo Muñoz
  • Thiago Serra
  • Calvin Tsay
    • Categories (Mixed) Integer Linear Programming, Optimization in Data Science, Polyhedra

    In the past decade, deep learning became the prevalent methodology for predictive modeling thanks to the remarkable accuracy of deep neural networks in tasks such as computer vision and natural language processing. Meanwhile, the structure of neural networks converged back to simpler representations based on piecewise constant and piecewise linear functions such as the Rectified … Read more

    First-Order Methods for Nonsmooth Nonconvex Functional Constrained Optimization with or without Slater Points

  • Zhichao Jia
  • Benjamin Grimmer
    • Categories Constrained Nonlinear Optimization, Nonsmooth Optimization Tags , , , ,

    Constrained optimization problems where both the objective and constraints may be nonsmooth and nonconvex arise across many learning and data science settings. In this paper, we show a simple first-order method finds a feasible, ϵ-stationary point at a convergence rate of O(ϵ−4) without relying on compactness or Constraint Qualification (CQ). When CQ holds, this convergence is measured by … Read more

    A Stochastic-Gradient-based Interior-Point Algorithm for Solving Smooth Bound-Constrained Optimization Problems

  • Qi Wang
  • Frank E. Curtis
  • Daniel P. Robinson
  • Vyacheslav Kungurtsev
    • Categories Constrained Nonlinear Optimization, Nonlinear Optimization, Optimization in Data Science Tags , ,

    A stochastic-gradient-based interior-point algorithm for minimizing a continuously differentiable objective function (that may be nonconvex) subject to bound constraints is presented, analyzed, and demonstrated through experimental results. The algorithm is unique from other interior-point methods for solving smooth (nonconvex) optimization problems since the search directions are computed using stochastic gradient estimates. It is also unique … Read more

    A New Inexact Proximal Linear Algorithm with Adaptive Stopping Criteria for Robust Phase Retrieval

  • Zhong Zheng
  • Shiqian Ma
  • Lingzhou Xue
    • Categories Nonlinear Optimization, Nonsmooth Optimization Tags , , , ,

    This paper considers the robust phase retrieval problem, which can be cast as a nonsmooth and nonconvex optimization problem. We propose a new inexact proximal linear algorithm with the subproblem being solved inexactly. Our contributions are two adaptive stopping criteria for the subproblem. The convergence behavior of the proposed methods is analyzed. Through experiments on … Read more

    A minimal face constant rank constraint qualification for reducible conic programming

  • Roberto Andreani
  • Gabriel Haeser
  • leomakoto
  • Héctor Ramírez
    • Categories Cone Programming, Linear, Cone and Semidefinite Programming, Nonlinear Optimization Tags , , , ,

    \(\) In a previous paper [R. Andreani, G. Haeser, L. M. Mito, H. Ramírez, T. P. Silveira. First- and second-order optimality conditions for second-order cone and semidefinite programming under a constant rank condition. Mathematical Programming, 2023. DOI: 10.1007/s10107-023-01942-8] we introduced a constant rank constraint qualification for nonlinear semidefinite and second-order cone programming by considering all … Read more