projected gradient method

Addressing Hierarchical Jointly-Convex Generalized Nash Equilibrium Problems with Nonsmooth Payoffs

  • Lorenzo Lampariello
  • Simone Sagratella
  • Valerio Giuseppe Sasso
    • Categories Convex and Nonsmooth Optimization Tags , , , , ,

    We consider a Generalized Nash Equilibrium Problem whose joint feasible region is implicitly defined as the solution set of another Nash game. This structure arises e.g. in multi-portfolio selection contexts, whenever agents interact at different hierarchical levels. We consider nonsmooth terms in all players’ objectives, to promote, for example, sparsity in the solution. Under standard … Read more

    A Novel Stepsize for Gradient Descent Method

  • Pham Thi Hoai
    • Categories Constrained Nonlinear Optimization, Convex Optimization, Nonlinear Optimization Tags , , , ,

    In this paper, we propose a novel stepsize for the classical gradient descent scheme to solve unconstrained nonlinear optimization problems. We are concerned with the convex and smooth objective without the globally Lipschitz gradient condition. Our new method just needs the locally Lipschitz gradient but still gets the rate $O(\frac{1}{k})$ of $f(x^k)-f_*$ at most. By … Read more

    Accelerated projected gradient algorithms for sparsity constrained optimization problems

  • Jan Harold Alcantara
  • Ching-pei Lee
    • Categories Constrained Nonlinear Optimization, Nonlinear Optimization Tags , ,

    We consider the projected gradient algorithm for the nonconvex best subset selection problem that minimizes a given empirical loss function under an \(\ell_0\)-norm constraint. Through decomposing the feasible set of the given sparsity constraint as a finite union of linear subspaces, we present two acceleration schemes with global convergence guarantees, one by same-space extrapolation and … Read more

    Complexity of a Projected Newton-CG Method for Optimization with Bounds

  • Yue Xie
  • Stephen Wright
    • Categories Bound-constrained Optimization Tags , , , ,

    This paper describes a method for solving smooth nonconvex minimization problems subject to bound constraints with good worst-case complexity and practical performance. The method contains elements of two existing methods: the classical gradient projection approach for bound-constrained optimization and a recently proposed Newton-conjugate gradient algorithm for unconstrained nonconvex optimization. Using a new definition of approximate … Read more

    An extension of the projected gradient method to a Banach space setting with application in structural topology optimization

  • Luise Blank
  • Christoph Rupprecht
    • Categories Constrained Nonlinear Optimization, Control Applications, Systems governed by Differential Equations Optimization Tags , , , ,

    For the minimization of a nonlinear cost functional under convex constraints the relaxed projected gradient process is a well known method. The analysis is classically performed in a Hilbert space. We generalize this method to functionals which are differentiable in a Banach space. The search direction is calculated by a quadratic approximation of the cost … Read more

    Approximation of Matrix Rank Function and Its Application to Matrix Rank Minimization

  • Chengjin Li
    • Categories Constrained Nonlinear Optimization, Linear, Cone and Semidefinite Programming Tags , , ,

    Matrix rank minimization problem is widely applicable in many fields such as control, signal processing and system identification. However, the problem is in general NP-hard, and it is computationally hard to solve directly in practice. In this paper, we provide a new kind of approximation functions for the rank of matrix, and the corresponding approximation … Read more

    Inexact projected gradient method for vector optimization

  • Ellen H. Fukuda
  • L. M. Graña Drummond
    • Categories Constrained Nonlinear Optimization, Multi-Criteria Optimization Tags , , ,

    In this work, we propose an inexact projected gradient-like method for solving smooth constrained vector optimization problems. In the unconstrained case, we retrieve the steepest descent method introduced by Graña Drummond and Svaiter. In the constrained setting, the method we present extends the exact one proposed by Graña Drummond and Iusem, since it admits relative … Read more

    On the convergence of the projected gradient method for vector optimization

  • Ellen H. Fukuda
  • L. M. Graña Drummond
    • Categories Constrained Nonlinear Optimization, Multi-Criteria Optimization Tags , , ,

    In 2004, Graña Drummond and Iusem proposed an extension of the projected gradient method for constrained vector optimization problems. In that method, an Armijo-like rule, implemented with a backtracking procedure, was used in order to determine the steplengths. The authors just showed stationarity of all cluster points and, for another version of the algorithm (with … Read more