(Mixed) Integer Nonlinear Programming

Successive Quadratic Upper-Bounding for Discrete Mean-Risk Minimization and Network Interdiction

  • Alper Atamturk
  • Carlos Deck
  • Hyemin Jeon
    • Categories (Mixed) Integer Nonlinear Programming, Robust Optimization Tags , , , ,

    The advances in conic optimization have led to its increased utilization for modeling data uncertainty. In particular, conic mean-risk optimization gained prominence in probabilistic and robust optimization. Whereas the corresponding conic models are solved efficiently over convex sets, their discrete counterparts are intractable. In this paper, we give a highly effective successive quadratic upper-bounding procedure … Read more

    Submodularity in conic quadratic mixed 0-1 optimization

  • Alper Atamturk
  • Andrés Gómez
    • Categories (Mixed) Integer Nonlinear Programming, Cutting Plane Approaches Tags , , , , , , ,

    We describe strong convex valid inequalities for conic quadratic mixed 0-1 optimization. These inequalities can be utilized for solving numerous practical nonlinear discrete optimization problems from value-at-risk minimization to queueing system design, from robust interdiction to assortment optimization through appropriate conic quadratic mixed 0-1 relaxations. The inequalities exploit the submodularity of the binary restrictions and … Read more

    Intersection cuts for factorable MINLP

  • Felipe Serrano
    • Categories (Mixed) Integer Nonlinear Programming, Global Optimization Tags , ,

    Given a factorable function f, we propose a procedure that constructs a concave underestimor of f that is tight at a given point. These underestimators can be used to generate intersection cuts. A peculiarity of these underestimators is that they do not rely on a bounded domain. We propose a strengthening procedure for the intersection … Read more

    Insight into the computation of Steiner minimal trees in Euclidean space of general dimension

  • Marcia Fampa
    • Categories (Mixed) Integer Nonlinear Programming, Global Optimization

    We present well known properties related to the topology of Steiner minimal trees and to the geometric position of Steiner points, and investigate their application in the main exact algorithms that have been proposed for the Euclidean Steiner problem. We discuss the difficulty in the application of properties that were very successfully applied to solve … Read more

    Convergence of Finite-Dimensional Approximations for Mixed-Integer Optimization with Differential Equations

  • Falk M. Hante
  • Martin Schmidt
    • Categories (Mixed) Integer Nonlinear Programming, Nonlinear Optimization, Systems governed by Differential Equations Optimization Tags , , , ,

    We consider a direct approach to solve mixed-integer nonlinear optimization problems with constraints depending on initial and terminal conditions of an ordinary differential equation. In order to obtain a finite-dimensional problem, the dynamics are approximated using discretization methods. In the framework of general one-step methods, we provide sufficient conditions for the convergence of this approach … Read more

    Generating feasible points for mixed-integer convex optimization problems by inner parallel cuts

  • Christoph Neumann
  • Oliver Stein
    • Categories (Mixed) Integer Nonlinear Programming, Cutting Plane Approaches Tags , , ,

    In this article we introduce an inner parallel cutting plane method (IPCP) to compute good feasible points along with valid cutting planes for mixed-integer convex optimization problems. The method iteratively generates polyhedral outer approximations of an enlarged inner parallel set (EIPS) of the continuously relaxed feasible set. This EIPS possesses the crucial property that any … Read more

    Sparse and Smooth Signal Estimation: Convexification of L0 Formulations

  • Alper Atamturk
  • Andrés Gómez
  • Shaoning Han
    • Categories (Mixed) Integer Nonlinear Programming Tags , , , ,

    Signal estimation problems with smoothness and sparsity priors can be naturally modeled as quadratic optimization with L0-“norm” constraints. Since such problems are non-convex and hard-to-solve, the standard approach is, instead, to tackle their convex surrogates based on L1-norm relaxations. In this paper, we propose new iterative conic quadratic relaxations that exploit not only the L0-“norm” … Read more

    Scoring positive semidefinite cutting planes for quadratic optimization via trained neural networks

  • Radu Baltean-Lugojan
  • Pierre Bonami
  • Ruth Misener
  • Andrea Tramontani
    • Categories (Mixed) Integer Nonlinear Programming, Global Optimization, Quadratic Programming Tags , , , , ,

    Semidefinite programming relaxations complement polyhedral relaxations for quadratic optimization, but global optimization solvers built on polyhedral relaxations cannot fully exploit this advantage. This paper develops linear outer-approximations of semidefinite constraints that can be effectively integrated into global solvers. The difference from previous work is that our proposed cuts are (i) sparser with respect to the … Read more

    Compact Disjunctive Approximations to Nonconvex Quadratically Constrained Programs

  • Hongbo Dong
  • Yunqi Luo
    • Categories (Mixed) Integer Nonlinear Programming, Global Optimization Theory, Second-Order Cone Programming

    Decades of advances in mixed-integer linear programming (MILP) and recent development in mixed-integer second-order-cone programming (MISOCP) have translated very mildly to progresses in global solving nonconvex mixed-integer quadratically constrained programs (MIQCP). In this paper we propose a new approach, namely Compact Disjunctive Approximation (CDA), to approximate nonconvex MIQCP to arbitrary precision by convex MIQCPs, which … Read more

    Submodularity and valid inequalities in nonlinear optimization with indicator variables

  • Andrés Gómez
    • Categories (Mixed) Integer Nonlinear Programming, Cutting Plane Approaches Tags , , ,

    We propose a new class of valid inequalities for mixed-integer nonlinear optimization problems with indicator variables. The inequalities are obtained by lifting polymatroid inequalities in the space of the 0–1 variables into conic inequalities in the original space of variables. The proposed inequalities are shown to describe the convex hull of the set under study … Read more