Contact

Email: pstuckey@unimelb.edu.au
Room: 6.19
Address: Doug Mcdonnell Building, Building 168, University of Melbourne, 3010 Australia
Phone: +613-8344-1341
Fax: +613-9349-4596

Peter Stuckey

Admin + Teaching

Seconded to NICTA Victoria Laboratory

My teaching commitments for 2015 are

Research + Service

My research interests include constraint programming, logic programming, discrete optimization, and program analysis. Other things of interest:

Constraints (postscript or PDF) electronic submissions for TPLP can be sent to pstuckey@unimelb.edu.au Remember to submit name, email address and postal addresses as well as an abstract and reasonably accurate word count.


Some invited talks

  • ICLP/CP 2005 "G12: From Solver Independent Models to Efficient Solutions" Powerpoint PDF
  • APLAS 2006 "Type Processing by Constraint Reasoning" PDF
  • Reformulation and Modelling Workshop CP2007 "Trials and Tribulations of Designing a Modelling Language" PDF
  • CPAIOR 2010 "Lazy Clause Generation: Combining the best of SAT and CP (and MIP?) solving" PDF
  • SAT 2013 "There are no CNF problems" PDF
  • CP 2013 "Those who forget the past are condemned to repeat it" PDF
  • PPDP 2013 "Search is dead, long live proof!" PDF
  • LaSH 2014 "Laziness is next to Godliness" PDF

Results for Specific Problems


Next Generation Optimization

I lead NICTA's Optimization research group project in Optimisation Platforms. We are developing the next generation of discrete optimization technology:
  • High level modelling language MiniZinc allows concise and powerful modelling of problems, and solving of the same model by many solvers: constraint programming, mixed integer programming, SAT, SMT and local search.
  • Powerful solving technology lazy clause generation provides the state-of-the-art constraint programming solvers, and unbeatable results on many problems, particularly scheduling.
  • Nested constraint programming: an extension to CP to allow the expression and solving of complex nested discrete optimization problems, such as minimax problems, stochastic optimization problems, bi-level and multi-level optimization, quantified constraint optimization problems, and more.

This page, its contents and style, are the responsibility of the author and do not necessarily represent the view, policies or opinions of The University of Melbourne. DISCLAIMER Except the last sentence which is included only because it is the policy of The University of Melbourne.

Created: 19 June 1995
Last modified 12th August 2015

Maintainer: Peter Stuckey,