Quadratic Programming

On the convexification of constrained quadratic optimization problems with indicator variables

  • Andrés Gómez
  • Simge Küçükyavuz
  • Linchuan Wei
    • Categories (Mixed) Integer Nonlinear Programming, Quadratic Programming Tags , , , ,

    Motivated by modern regression applications, in this paper, we study the convexification of quadratic optimization problems with indicator variables and combinatorial constraints on the indicators. Unlike most of the previous work on convexification of sparse regression problems, we simultaneously consider the nonlinear objective, indicator variables, and combinatorial constraints. We prove that for a separable quadratic … Read more

    Outer Approximation for Global Optimization of Mixed-Integer Quadratic Bilevel Problems

  • Veronika Grimm
  • Thomas Kleinert
  • Martin Schmidt
    • Categories (Mixed) Integer Nonlinear Programming, Quadratic Programming Tags , , ,

    Bilevel optimization problems have received a lot of attention in the last years and decades. Besides numerous theoretical developments there also evolved novel solution algorithms for mixed-integer linear bilevel problems and the most recent algorithms use branch-and-cut techniques from mixed-integer programming that are especially tailored for the bilevel context. In this paper, we consider MIQP-QP … Read more

    A New Preconditioning Approach for an Interior Point-Proximal Method of Multipliers for Linear and Convex Quadratic Programming

  • Luca Bergamaschi
  • Jacek Gondzio
  • John W. Pearson
  • Ángeles Martínez
  • Spyridon Pougkakiotis
    • Categories Convex Optimization, Linear Programming, Quadratic Programming Tags , , , ,

    In this paper, we address the efficient numerical solution of linear and quadratic programming problems, often of large scale. With this aim, we devise an infeasible interior point method, blended with the proximal method of multipliers, which in turn results in a primal-dual regularized interior point method. Application of this method gives rise to a … Read more

    On the tightness of SDP relaxations of QCQPs

  • Fatma Kılınç-Karzan
  • Alex L. Wang
    • Categories Global Optimization Theory, Quadratic Programming, Semi-definite Programming Tags , , ,

    Quadratically constrained quadratic programs (QCQPs) are a fundamental class of optimization problems well-known to be NP-hard in general. In this paper we study conditions under which the standard semidefinite program (SDP) relaxation of a QCQP is tight. We begin by outlining a general framework for proving such sufficient conditions. Then using this framework, we show … Read more

    Exploiting Aggregate Sparsity in Second Order Cone Relaxations for Quadratic Constrained Quadratic Programming Problems

  • Heejune Sheen
  • Makoto Yamashita
    • Categories Quadratic Programming, Second-Order Cone Programming, Semi-definite Programming Tags , , , ,

    Among many approaches to increase the computational efficiency of semidefinite programming (SDP) relaxation for quadratic constrained quadratic programming problems (QCQPs), exploiting the aggregate sparsity of the data matrices in the SDP by Fukuda et al. (2001) and second-order cone programming (SOCP) relaxation have been popular. In this paper, we exploit the aggregate sparsity of SOCP … Read more

    An algorithm for optimization with disjoint linear constraints and its application for predicting rain

  • Tijana Janjic
  • Philippe L. Toint
  • Yvonne Ruckstuhl
    • Categories Basic Sciences Applications, Quadratic Programming Tags ,

    A specialized algorithm for quadratic optimization (QO, or, formerly, QP) with disjoint linear constraints is presented. In the considered class of problems, a subset of variables are subject to linear equality constraints, while variables in a different subset are constrained to remain in a convex set. The proposed algorithm exploits the structure by combining steps … Read more

    Tight compact extended relaxations for nonconvex quadratic programming problems with box constraints

  • Sven de Vries
  • Bernd Perscheid
    • Categories (Mixed) Integer Nonlinear Programming, Graphs and Matroids, Quadratic Programming Tags ,

    Cutting planes from the Boolean Quadric Polytope (BQP) can be used to reduce the optimality gap of the NP-hard nonconvex quadratic program with box constraints (BoxQP). It is known that all cuts of the Chvátal-Gomory closure of the BQP are A-odd cycle inequalities. We obtain a compact extended relaxation of all A-odd cycle inequalities, which … Read more

    On the asymptotic convergence and acceleration of gradient methods

  • Yu-Hong Dai
  • Yakui Huang
  • Xin-Wei Liu
  • Hongchao Zhang
    • Categories Convex Optimization, Quadratic Programming, Unconstrained Optimization Tags , , , , , ,

    We consider the asymptotic behavior of a family of gradient methods, which include the steepest descent and minimal gradient methods as special instances. It is proved that each method in the family will asymptotically zigzag between two directions. Asymptotic convergence results of the objective value, gradient norm, and stepsize are presented as well. To accelerate … Read more

    Random projections for quadratic programs

  • Claudia D'Ambrosio
  • Leo Liberti
  • Pierre-Louis Poirion
  • Ky Vu
    • Categories Quadratic Programming Tags , , , ,

    Random projections map a set of points in a high dimensional space to a lower dimen- sional one while approximately preserving all pairwise Euclidean distances. While random projections are usually applied to numerical data, we show they can be successfully applied to quadratic programming formulations over a set of linear inequality constraints. Instead of solving … Read more

    Sparse PCA on fixed-rank matrices

  • Alberto Del Pia
    • Categories Global Optimization Theory, Quadratic Programming Tags , , , ,

    Sparse PCA is the optimization problem obtained from PCA by adding a sparsity constraint on the principal components. Sparse PCA is NP-hard and hard to approximate even in the single-component case. In this paper we settle the computational complexity of sparse PCA with respect to the rank of the covariance matrix. We show that, if … Read more