Convex Optimization

Efficient parameter-free restarted accelerated gradient methods for convex and strongly convex optimization

  • Arnesh Sujanani
  • Renato D.C. Monteiro
    • Categories Convex and Nonsmooth Optimization, Convex Optimization, Nonlinear Optimization Tags , , , , ,

    This paper develops a new parameter-free restarted method, namely RPF-SFISTA, and a new parameter-free aggressive regularization method, namely A-REG, for solving strongly convex and convex composite optimization problems, respectively. RPF-SFISTA has the major advantage that it requires no knowledge of both the strong convexity parameter of the entire composite objective and the Lipschitz constant of … Read more

    Accessible Theoretical Complexity of the Restarted Primal-Dual Hybrid Gradient Method for Linear Programs with Unique Optima

  • Zikai Xiong
    • Categories Convex Optimization, Linear Programming, Nonsmooth Optimization Tags , , ,

    The restarted primal-dual hybrid gradient method (rPDHG) has recently emerged as an important tool for solving large-scale linear programs (LPs). For LPs with unique optima, we present an iteration bound of \(\widetilde{O}\left(\kappa\Phi\cdot\ln\left(\frac{\|w^*\|}{\varepsilon}\right)\right)\), where \(\varepsilon\) is the target tolerance, \(\kappa\) is the standard matrix condition number, \(\|w^*\|\) is the norm of the optimal solution, and \(\Phi\) … Read more

    Dual Spectral Projected Gradient Method for Generalized Log-det Semidefinite Programming

  • Charles Namchaisiri
  • Makoto Yamashita
    • Categories Convex Optimization, Linear, Cone and Semidefinite Programming, Semi-definite Programming Tags , ,

    Log-det semidefinite programming (SDP) problems are optimization problems that often arise from Gaussian graphic models. A log-det SDP problem with an l1-norm term has been examined in many methods, and the dual spectral projected gradient (DSPG) method by Nakagaki et al.~in 2020 is designed to efficiently solve the dual problem of the log-det SDP by … Read more

    Universal subgradient and proximal bundle methods for convex and strongly convex hybrid composite optimization

  • Vincent Guigues
  • Jiaming Liang
  • Renato D.C. Monteiro
    • Categories Convex Optimization, Nonsmooth Optimization Tags , , ,

    This paper develops two parameter-free methods for solving convex and strongly convex hybrid composite optimization problems, namely, a composite subgradient type method and a proximal bundle type method. Both functional and stationary complexity bounds for the two methods are established in terms of the unknown strong convexity parameter. To the best of our knowledge, the … Read more

    Generalized Ellipsoids

  • Abraar Chaudhry
  • Amir Ali Ahmadi
  • Cemil Dibek
    • Categories Convex and Nonsmooth Optimization, Convex Optimization, Linear, Cone and Semidefinite Programming Tags , , , ,

    We introduce a family of symmetric convex bodies called generalized ellipsoids of degree \(d\) (GE-\(d\)s), with ellipsoids corresponding to the case of \(d=0\). Generalized ellipsoids (GEs) retain many geometric, algebraic, and algorithmic properties of ellipsoids. We show that the conditions that the parameters of a GE must satisfy can be checked in strongly polynomial time, … Read more

    On the strength of Burer’s lifted convex relaxation to quadratic programming with ball constraints

  • Fatma Kılınç-Karzan
  • Shengding Sun
    • Categories Convex Optimization, Quadratic Programming, Semi-definite Programming

    We study quadratic programs with m ball constraints, and the strength of a lifted convex relaxation for it recently proposed by Burer (2024). Burer shows this relaxation is exact when m=2. For general m, Burer (2024) provides numerical evidence that this lifted relaxation is tighter than the Kronecker product based Reformulation Linearization Technique (RLT) inequalities … Read more

    A combinatorial approach to Ramana’s exact dual for semidefinite programming

  • Gabor Pataki
    • Categories Convex Optimization, Linear, Cone and Semidefinite Programming, Semi-definite Programming Tags , , ,

    Thirty years ago, in a seminal paper Ramana derived an exact dual for Semidefinite Programming (SDP). Ramana’s dual has the following remarkable features: i) it assumes feasibility of the primal, but it does not make any regularity assumptions, such as strict feasibility ii) its optimal value is the same as the optimal value of the … Read more

    A Facial Reduction Algorithm for Standard Spectrahedra

  • Haesol Im
    • Categories Convex Optimization, Semi-definite Programming

    Facial reduction is a pre-processing method aimed at reformulating a problem to ensure strict feasibility. The importance of constructing a robust model is widely recognized in the literature, and facial reduction has emerged an attractive approach for achieving robustness. In this note, we outline a facial reduction algorithm for a standard spectrahedra, the intersection of … Read more

    A Subgradient Projection Method with Outer Approximation for Solving Semidefinite Programming Problems

  • Nagisa Sugishita
  • Miguel F. Anjos
    • Categories Convex Optimization, Semi-definite Programming Tags , ,

    We explore the combination of subgradient projection with outer approximation to solve semidefinite programming problems. We compare several ways to construct outer approximations using the problem structure. The resulting approach enjoys the strengths of both subgradient projection and outer approximation methods. Preliminary computational results on the semidefinite programming relaxations of graph partitioning and max-cut show … Read more

    Projection onto hyperbolicity cones and beyond: a dual Frank-Wolfe approach

  • Takayuki Nagano
  • Bruno F. Lourenço
  • Akiko Takeda
    • Categories Cone Programming, Convex Optimization, Linear, Cone and Semidefinite Programming Tags , , , ,

    We discuss the problem of projecting a point onto an arbitrary hyperbolicity cone from both theoretical and numerical perspectives. While hyperbolicity cones are furnished with a generalization of the notion of eigenvalues, obtaining closed form expressions for the projection operator as in the case of semidefinite matrices is an elusive endeavour. To address that we … Read more