Sunyoung Kim

T-semidefinite programming relaxation with third-order tensors for constrained polynomial optimization

  • Hiroki Marumo
  • Sunyoung Kim
  • Makoto Yamashita
    • Categories Linear, Cone and Semidefinite Programming, Semi-definite Programming Tags , , , , ,

    We study T-semidefinite programming (SDP) relaxation for constrained polynomial optimization problems (POPs). T-SDP relaxation for unconstrained POPs was introduced by Zheng, Huang and Hu in 2022. In this work, we propose a T-SDP relaxation for POPs with polynomial inequality constraints and show that the resulting T-SDP relaxation formulated with third-order tensors can be transformed into … Read more

    An Exceptionally Difficult Binary Quadratic Optimization Problem with Symmetry: a Challenge for The Largest Unsolved QAP Instance Tai256c

  • Koichi Fujii
  • Sunyoung Kim
  • Masakazu Kojima
  • Hans D Mittelmann
  • Yuji Shinano
    • Categories 0-1 Programming, Quadratic Programming, Semi-definite Programming Tags , , , , ,

    Tai256c is the largest unsolved quadratic assignment problem (QAP) instance in QAPLIB. It is known that QAP tai256c can be converted into a 256 dimensional binary quadratic optimization problem (BQOP) with a single cardinality constraint which requires the sum of the binary variables to be 92. As the BQOP is much simpler than the original … Read more

    Exact Matrix Completion via High-Rank Matrices in Sum-of-Squares Relaxations

  • Sunyoung Kim
  • Godai Azuma
  • Makoto Yamashita
    • Categories Convex Optimization, Global Optimization Theory, Semi-definite Programming Tags , , , ,

    We study exact matrix completion from partially available data with hidden connectivity patterns. Exact matrix completion was shown to be possible recently by Cosse and Demanet in 2021 with Lasserre’s relaxation using the trace of the variable matrix as the objective function with given data structured in a chain format. In this study, we introduce … Read more

    Further Development in Convex Conic Reformulation of Geometric Nonconvex Conic Optimization Problems

  • Naohiko Arima
  • Sunyoung Kim
  • Masakazu Kojima
    • Categories Global Optimization, Linear, Cone and Semidefinite Programming, Semi-definite Programming Tags , , , , ,

    \(\) A geometric nonconvex conic optimization problem (COP) was recently proposed by Kim, Kojima and Toh asa unified framework for convex conic reformulation of a class of quadratic optimization problems and polynomial optimization problems. The nonconvex COP minimizes a linear function over the intersection of a nonconvex cone K, a convex subcone J of the … Read more

    Equivalent Sufficient Conditions for Global Optimality of Quadratically Constrained Quadratic Program

  • Sunyoung Kim
  • Masakazu Kojima
    • Categories Global Optimization, Linear, Cone and Semidefinite Programming, Semi-definite Programming Tags , , , , ,

    \(\) We study the equivalence of several well-known sufficient optimality conditions for a general quadratically constrained quadratic program (QCQP). The conditions are classified in two categories. The first one is for determining an optimal solution and the second one is for finding an optimal value. The first category of conditions includes the existence of a … Read more

    The Largest Unsolved QAP Instance Tai256c Can Be Converted into A 256-dimensional Simple BQOP with A Single Cardinality Constraint

  • Koichi Fujii
  • Sunyoung Kim
  • Masakazu Kojima
  • Hans D Mittelmann
  • Yuji Shinano
    • Categories 0-1 Programming, Branch and Cut Algorithms, Semi-definite Programming Tags , , , , ,

    Tai256c is the largest unsolved quadratic assignment problem (QAP) instance in QAPLIB; a 1.48\% gap remains between the best known feasible objective value and lower bound of the unknown optimal value. This paper shows that the instance can be converted into a 256 dimensional binary quadratic optimization problem (BQOP) with a single cardinality constraint which … Read more

    Solving Challenging Large Scale QAPs

  • Koichi Fujii
  • Naoki Ito
  • Masakazu Kojima
  • Kim-Chuan Toh
  • Sunyoung Kim
  • Yuji Shinano
    • Categories Applications - OR and Management Sciences Tags ,

    We report our progress on the project for solving larger scale quadratic assignment problems (QAPs). Our main approach to solve large scale NP-hard combinatorial optimization problems such as QAPs is a parallel branch-and-bound method eciently implemented on a powerful computer system using the Ubiquity Generator (UG) framework that can utilize more than 100,000 cores. Lower … Read more