Citation
Carlo Fischione. "Fast-Lipschitz Optimization". Talk or presentation, 11, September, 2012.

Abstract
In many optimization problems, decision variables must be computed by algorithms that need to be fast, simple, and robust to errors and noises, both in a centralized and in a distributed set-up. This occurs, for example, in contract based design, sensors networks, smart grids, water distribution, and vehicular networks. In this seminar, a new simple optimization theory, named Fast-Lipschitz optimization, is presented for a novel class of both convex and non-convex scalar and multi-objective optimization problems that are pervasive in the systems mentioned above. Fast-Lipschitz optimization can be applied to both centralized and distributed optimization. Fast-Lipchitz optimization solvers exhibit a low computational and communication complexity when compared to existing solution methods. In particular, compared to traditional Lagrangian methods, which often converge linearly, the convergence time of centralized Fast-Lipschitz algorithms is superlinear. Distributed Fast-Lipschitz algorithms converge fast, as opposed to traditional Lagrangian decomposition and parallelization methods, which generally converge slowly and at the price of many message passings among the nodes. In both cases, the computational complexity is much lower than traditional Lagrangian methods. Fast-Lipschitz optimization is then illustrated by distributed estimation and detection applications in wireless sensor networks.

Electronic downloads

• dreams-11-09-2012.pdf · application/download · 4548 kbytes

• HTML

  Carlo Fischione. <a
  href="http://chess.eecs.berkeley.edu/pubs/935.html"
  ><i>Fast-Lipschitz
  Optimization</i></a>, Talk or presentation,  11,
  September, 2012.

• Plain text

  Carlo Fischione. "Fast-Lipschitz Optimization".
  Talk or presentation,  11, September, 2012.

• BibTeX

  @presentation{Fischione12_FastLipschitzOptimization,
      author = {Carlo Fischione},
      title = {Fast-Lipschitz Optimization},
      day = {11},
      month = {September},
      year = {2012},
      abstract = {In many optimization problems, decision variables
                must be computed by algorithms that need to be
                fast, simple, and robust to errors and noises,
                both in a centralized and in a distributed set-up.
                This occurs, for example, in contract based
                design, sensors networks, smart grids, water
                distribution, and vehicular networks. In this
                seminar, a new simple optimization theory, named
                Fast-Lipschitz optimization, is presented for a
                novel class of both convex and non-convex scalar
                and multi-objective optimization problems that are
                pervasive in the systems mentioned above.
                Fast-Lipschitz optimization can be applied to both
                centralized and distributed optimization.
                Fast-Lipchitz optimization solvers exhibit a low
                computational and communication complexity when
                compared to existing solution methods. In
                particular, compared to traditional Lagrangian
                methods, which often converge linearly, the
                convergence time of centralized Fast-Lipschitz
                algorithms is superlinear. Distributed
                Fast-Lipschitz algorithms converge fast, as
                opposed to traditional Lagrangian decomposition
                and parallelization methods, which generally
                converge slowly and at the price of many message
                passings among the nodes. In both cases, the
                computational complexity is much lower than
                traditional Lagrangian methods. Fast-Lipschitz
                optimization is then illustrated by distributed
                estimation and detection applications in wireless
                sensor networks. },
      URL = {http://chess.eecs.berkeley.edu/pubs/935.html}
  }
  

Posted by David Broman on 12 Sep 2012.
For additional information, see the Publications FAQ or contact webmaster at chess eecs berkeley edu.