{
  "id": "2103.13560",
  "version": "v1",
  "published": "2021-03-25T01:49:10.000Z",
  "updated": "2021-03-25T01:49:10.000Z",
  "title": "Distributed and Asynchronous Algorithms for N-block Convex Optimization with Coupling Constraints",
  "authors": [
    "Run Chen",
    "Andrew L. Liu"
  ],
  "categories": [
    "math.OC"
  ],
  "abstract": "This paper first proposes an N-block PCPM algorithm to solve N-block convex optimization problems with both linear and nonlinear constraints, with global convergence established. A linear convergence rate under the strong second-order conditions for optimality is observed in the numerical experiments. Next, for a starting point, an asynchronous N-block PCPM algorithm is proposed to solve linearly constrained N-block convex optimization problems. The numerical results demonstrate the sub-linear convergence rate under the bounded delay assumption, as well as the faster convergence with more short-time iterations than a synchronous iterative scheme.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2021-03-25T01:49:10.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "coupling constraints",
      "asynchronous algorithms",
      "n-block pcpm algorithm",
      "constrained n-block convex optimization problems",
      "strong second-order conditions"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}