Campus Event Calendar
Event Entry
New for: D3
What and Who
On Warmstarting Interior-Point Methods
Miguel F. Anjos
University of Waterloo
Lecture
Miguel F. Anjos is an Associate Professor in the Department of Management Sciences at the University of Waterloo, Canada. He received his Ph.D. in Combinatorics & Optimization in 2001 under the supervision of Henry Wolkowicz. His research interests are in optimization and mathematical programming, and he is particularly interested in their application to engineering problems. He is Associate Editor of Discrete Applied
Mathematics and a member of the editorial board of Optimization and Engineering. He is also a member of the Research Review Committee of MITACS, the Canadian Network of Centres of Excellence for the Mathematical Sciences. He has collaborated on industrial research projects in the areas of revenue management (sponsored by British Midland Airways), network planning (sponsored by Bell Canada), and ambulance deployment (sponsored
by the Region of Waterloo Emergency Medical Services). He was awarded a Humboldt Research Fellowship for Experienced Researchers in 2009.
AG 1, AG 4, RG1, MMCI, AG 3, AG 5, SWS
AG Audience
English
Note: We use this to send email in the morning.
Date, Time and Location
Friday, 29 January 2010
13:00
30 Minutes
E1 4
024
Saarbrücken
Abstract
Despite the many advantages of interior-point algorithms over active-set
methods for linear optimization, one of the remaining practical challenges
is to efficiently solve series of related problems by an effective
warmstarting strategy. We present a new warm-starting technique for
re-optimizing successive linear programming problems when using
interior-point methods. The idea is that a previously optimal solution can
be used as the initial point for re-starting by suitably relaxing the
non-negativity constraints using additional slack variables. The resulting
algorithm retains polynomial-time convergence, and computational results
show that the iteration savings can be up to 50% on average.
This is joint work with Alexander Engau (University of Colorado-Denver)
and Anthony Vannelli (University of Guelph).
methods for linear optimization, one of the remaining practical challenges
is to efficiently solve series of related problems by an effective
warmstarting strategy. We present a new warm-starting technique for
re-optimizing successive linear programming problems when using
interior-point methods. The idea is that a previously optimal solution can
be used as the initial point for re-starting by suitably relaxing the
non-negativity constraints using additional slack variables. The resulting
algorithm retains polynomial-time convergence, and computational results
show that the iteration savings can be up to 50% on average.
This is joint work with Alexander Engau (University of Colorado-Denver)
and Anthony Vannelli (University of Guelph).
Contact
Chien-Chung Huang
--email hidden
passcode not visible
logged in users only
Tags, Category, Keywords and additional notes
Interior-point methods; Warmstarting; Combinatorial optimization
Chien-Chung Huang, 01/02/2010 18:50
Chien-Chung Huang, 01/01/2010 17:45 -- Created document.
Chien-Chung Huang, 01/01/2010 17:45 -- Created document.