Convex Optimization

On smoothness properties of optimal value functions at the boundary of their domain under complete convexity

  • Oliver Stein
  • Nathan Sudermann-Merx
    • Categories Convex Optimization, Nonsmooth Optimization Tags , , , ,

    This article studies continuity and directional differentiability properties of optimal value functions, in particular at boundary points of their domain. We extend and complement standard continuity results from W.W. Hogan, Point-to-set maps in mathematical programming, SIAM Review, Vol. 15 (1973), 591-603, for abstract feasible set mappings under complete convexity as well as standard differentiability results … Read more

    Optimal parameter selection for the alternating direction method of multipliers (ADMM): quadratic problems

  • Euhanna Ghadimi
  • Mikael Johansson
  • Iman Shames
  • André Teixeira
    • Categories Convex Optimization, Quadratic Programming Tags , ,

    The alternating direction method of multipliers (ADMM) has emerged as a powerful technique for large-scale structured optimization. Despite many recent results on the convergence properties of ADMM, a quantitative characterization of the impact of the algorithm parameters on the convergence times of the method is still lacking. In this paper we find the optimal algorithm … Read more

    A branch and bound approach for convex semi-infinite programming

  • Le Thi Hoai An
  • Mohand Ouanes
  • A. Ismael F. Vaz
    • Categories Convex Optimization, Semi-infinite Programming

    In this paper we propose an efficient approach for globally solving a class of convex semi-infinite programming (SIP) problems. Under the objective function and constraints (w.r.t. the variables to be optimized) convexity assumption, and appropriate differentiability, we propose a branch and bound exchange type method for SIP. To compute a feasible point for a SIP … Read more

    Forward-Backward and Tseng’s Type Penalty Schemes for Monotone Inclusion Problems

  • Radu Ioan Bot
  • Ernö Robert Csetnek
    • Categories Convex Optimization Tags , , , , , , , ,

    We deal with monotone inclusion problems of the form $0\in Ax+Dx+N_C(x)$ in real Hilbert spaces, where $A$ is a maximally monotone operator, $D$ a cocoercive operator and $C$ the nonempty set of zeros of another cocoercive operator. We propose a forward-backward penalty algorithm for solving this problem which extends the one proposed by H. Attouch, … Read more

    Facially exposed cones are not always nice

  • Vera Roshchina
    • Categories Convex Optimization, Linear, Cone and Semidefinite Programming Tags , ,

    We address the conjecture proposed by Gabor Pataki that every facially exposed cone is nice. We show that the conjecture is true in the three-dimensional case, however, there exists a four-dimensional counterexample of a cone that is facially exposed but is not nice. Citation CRN, University of Ballarat Article Download View Facially exposed cones are … Read more

    On full Jacobian decomposition of the augmented Lagrangian method for separable convex programming

  • Bingsheng He
  • Xiao-Ming Yuan
  • Liusheng Hou
    • Categories Convex Optimization Tags , , , , , ,

    The augmented Lagrangian method (ALM) is a benchmark for solving the convex minimization problem with linear constraints. We consider the special case where the objective is in form of the sum of m functions without coupled variables. For solving this separable convex programming model, it is usually required to decompose the ALM subproblem at each … Read more

    Robust convex relaxation for the planted clique and densest k-subgraph problems

  • Brendan Ames
    • Categories Constrained Nonlinear Optimization, Convex Optimization, Nonsmooth Optimization Tags , , ,

    We consider the problem of identifying the densest k-node subgraph in a given graph. We write this problem as an instance of rank-constrained cardinality minimization and then relax using the nuclear and l1 norms. Although the original combinatorial problem is NP-hard, we show that the densest k-subgraph can be recovered from the solution of our … Read more

    Lagrangian Transformation and Interior Ellipsoid Methods in Convex Optimization

  • Roman Polyak
    • Categories Convex Optimization

    The rediscovery of the affine scaling method in the late 80s was one of the turning points which led to a new chapter in Modern Optimization – the interior point methods (IPMs). Simultaneously and independently the theory of exterior point methods for convex optimization arose. The two seemingly unconnected fields turned out to be intrinsically … Read more

    Minimal Residual Methods for Complex Symmetric, Skew Symmetric, and Skew Hermitian Systems

  • Sou-Cheng Choi
    • Categories Convex Optimization, Optimization Software and Modeling Systems, Robust Optimization Tags , , , , , , , , , , , ,

    While there is no lack of efficient Krylov subspace solvers for Hermitian systems, there are few for complex symmetric, skew symmetric, or skew Hermitian systems, which are increasingly important in modern applications including quantum dynamics, electromagnetics, and power systems. For a large consistent complex symmetric system, one may apply a non-Hermitian Krylov subspace method disregarding … Read more

    An interior proximal point method with phi-divergence for Equilibrium Problems

  • Arnaldo Brito
  • Paulo Roberto Oliveira
  • Paulo Santos
  • Afonso Silva
    • Categories Applications - OR and Management Sciences, Convex Optimization

    In this paper, we consider the problem of general equilibrium in a  finite-dimensional space on a closed convex set. For solving this problem, we developed an interior proximal point algorithm with phi-divergence. Under reasonable assumptions, we prove that the sequence generated by the algorithm converges to a solution of the Equilibrium Problem, when the regularization … Read more