augmented lagrangian method

Faster Lagrangian-based methods: a unified prediction-correction framework

  • Zhang Tao
  • Yong Xia
  • Li Shiru
    • Categories Convex Optimization Tags , , ,

    Motivated by the prediction-correction framework constructed by He and Yuan [SIAM J. Numer. Anal. 50: 700-709, 2012], we propose a unified prediction-correction framework to accelerate Lagrangian-based methods. More precisely, for strongly convex optimization, general linearized Lagrangian method with indefinite proximal term, alternating direction method of multipliers (ADMM) with the step size of Lagrangian multiplier not … Read more

    Partitioning through projections: strong SDP bounds for large graph partition problems

  • Frank de Meijer
  • Renata Sotirov
  • Angelika Wiegele
  • Shudian Zhao
    • Categories Semi-definite Programming Tags , , , ,

    The graph partition problem (GPP) aims at clustering the vertex set of a graph into a fixed number of disjoint subsets of given sizes such that the sum of weights of edges joining different sets is minimized. This paper investigates the quality of doubly nonnegative (DNN) relaxations, i.e., relaxations having matrix variables that are both … Read more

    A novel sequential optimality condition for smooth constrained optimization and algorithmic consequences

  • Renan W. Prado
  • Sandra A. Santos
  • Lucas E. A. Simões
    • Categories Constrained Nonlinear Optimization Tags , ,

    In the smooth constrained optimization setting, this work introduces the Domain Complementary Approximate Karush-Kuhn-Tucker (DCAKKT) condition, inspired by a sequential optimality condition recently devised for nonsmooth constrained optimization problems. It is shown that the augmented Lagrangian method can generate limit points satisfying DCAKKT, and it is proved that such a condition is not related to … Read more

    Global Convergence of Augmented Lagrangian Method Applied to Mathematical Program with Switching Constraints

  • Lei Guo
  • Gao-Xi Li
  • Xinmin Yang
    • Categories Complementarity and Variational Inequalities Tags , ,

    The mathematical program with switching constraints (MPSC) is a kind of problems with disjunctive constraints. The existing convergence results cannot directly be applied to this kind of problem since the required constraint qualifications for ensuring the convergence are very likely to fail. In this paper, we apply the augmented Lagrangian method (ALM) to solve the … Read more

    Global Complexity Bound of a Proximal ADMM for Linearly-Constrained Nonseperable Nonconvex Composite Programming

  • Weiwei Kong
  • Renato D.C. Monteiro
    • Categories Nonlinear Optimization, Nonsmooth Optimization Tags , , , , ,

    This paper proposes and analyzes a dampened proximal alternating direction method of multipliers (DP.ADMM) for solving linearly-constrained nonconvex optimization problems where the smooth part of the objective function is nonseparable. Each iteration of DP.ADMM consists of: (ii) a sequence of partial proximal augmented Lagrangian (AL) updates, (ii) an under-relaxed Lagrange multiplier update, and (iii) a … Read more

    Dual descent ALM and ADMM

  • Kaizhao Sun
  • Xu Andy Sun
    • Categories Constrained Nonlinear Optimization, Nonlinear Optimization Tags ,

    \(\) Classical primal-dual algorithms attempt to solve $\max_{\mu}\min_{x} \mathcal{L}(x,\mu)$ by alternatively minimizing over the primal variable $x$ through primal descent and maximizing the dual variable $\mu$ through dual ascent. However, when $\mathcal{L}(x,\mu)$ is highly nonconvex with complex constraints in $x$, the minimization over $x$ may not achieve global optimality, and hence the dual ascent step … Read more

    A New Insight on Augmented Lagrangian Method with Applications in Machine Learning

  • Jianchao Bai
    • Categories Convex Optimization Tags , , , ,

    By exploiting double-penalty terms for the primal subproblem, we develop a novel relaxed augmented Lagrangian method for solving a family of convex optimization problems subject to equality or inequality constraints. This new method is then extended to solve a general multi-block separable convex optimization problem, and two related primal-dual hybrid gradient algorithms are also discussed. … Read more

    Algorithms for Difference-of-Convex (DC) Programs Based on Difference-of-Moreau-Envelopes Smoothing

  • Xu Andy Sun
  • Kaizhao Sun
    • Categories Constrained Nonlinear Optimization, Nonlinear Optimization, Nonsmooth Optimization Tags , , ,

    In this paper we consider minimization of a difference-of-convex (DC) function with and without linear constraints. We first study a smooth approximation of a generic DC function, termed difference-of-Moreau-envelopes (DME) smoothing, where both components of the DC function are replaced by their respective Moreau envelopes. The resulting smooth approximation is shown to be Lipschitz differentiable, … Read more

    Decomposition Methods for Global Solutions of Mixed-Integer Linear Programs

  • Kaizhao Sun
  • Wotao Yin
  • Mou Sun
    • Categories (Mixed) Integer Linear Programming, Global Optimization Tags , ,

    This paper introduces two decomposition-based methods for two-block mixed-integer linear programs (MILPs), which aim to take advantage of separable structures of the original problem by solving a sequence of lower-dimensional MILPs. The first method is based on the $\ell_1$-augmented Lagrangian method (ALM), and the second one is based on a modified alternating direction method of … Read more

    Faster Lagrangian-Based Methods in Convex Optimization

  • Shoham Sabach
  • Marc Teboulle
    • Categories Convex Optimization, Nonsmooth Optimization Tags , , , , , ,

    In this paper, we aim at unifying, simplifying, and improving the convergence rate analysis of Lagrangian-based methods for convex optimization problems. We first introduce the notion of nice primal algorithmic map, which plays a central role in the unification and in the simplification of the analysis of all Lagrangian-based methods. Equipped with a nice primal … Read more