{
  "id": "1212.4701",
  "version": "v7",
  "published": "2012-12-19T15:12:55.000Z",
  "updated": "2014-09-25T14:30:55.000Z",
  "title": "On Solving Convex Optimization Problems with Linear Ascending Constraints",
  "authors": [
    "Zizhuo Wang"
  ],
  "comment": "20 pages. The final version of this paper is published in Optimization Letters",
  "categories": [
    "math.OC"
  ],
  "abstract": "In this paper, we propose two algorithms for solving convex optimization problems with linear ascending constraints. When the objective function is separable, we propose a dual method which terminates in a finite number of iterations. In particular, the worst case complexity of our dual method improves over the best-known result for this problem in Padakandla and Sundaresan [SIAM J. Optimization, 20 (2009), pp. 1185-1204]. We then propose a gradient projection method to solve a more general class of problems in which the objective function is not necessarily separable. Numerical experiments show that both our algorithms work well in test problems.",
  "revisions": [
    {
      "version": "v6",
      "updated": "2014-06-13T14:39:14.000Z",
      "comment": "20 pages",
      "journal": null,
      "doi": null
    },
    {
      "version": "v7",
      "updated": "2014-09-25T14:30:55.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "solving convex optimization problems",
      "linear ascending constraints",
      "dual method",
      "objective function",
      "gradient projection method"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 20,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2012arXiv1212.4701W"
    }
  }
}