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On Implementing a Parallel Integer Solver Using Optimization Services. Jun Ma Huanyuan (Wayne) Sheng Joint work with Sanjay Mehrotra. Outline. Brief Introduction Impact Generalized MIP Solver Optimization Services (OS)

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slide1

On Implementing a Parallel Integer Solver Using Optimization Services

Jun Ma

Huanyuan (Wayne) Sheng

Joint work with

Sanjay Mehrotra

Jun Ma, Sanjay Mehrotra and Huanyuan Sheng

Impact Solver for Optimization Services, November 8, 2006

outline
Outline
  • Brief Introduction
    • Impact Generalized MIP Solver
    • Optimization Services (OS)
  • Distributed Parallel For Integer Programming Using OS
  • Conclusion

Jun Ma, Sanjay Mehrotra and Huanyuan Sheng

Impact Solver for Optimization Services, November 8, 2006

introduction impact gmip solver
IntroductionImpact GMIP Solver
  • IMPACT -- Integrated Mathematical Programming Advanced Computational Tools
  • Features
    • Generalized Mixed Integer Nonlinear Solver (GMIP).
    • Generalized Hyperplanes based Branch and Bound.
    • Standalone Solver and Remote Solver Service.
    • Unified NATIVE Interface with Optimization Services.

Jun Ma, Sanjay Mehrotra and Huanyuan Sheng

Impact Solver for Optimization Services, November 8, 2006

impact gmip solver features algorithms
Impact GMIP Solver Features Algorithms
  • Research Focus
    • Mixed Integer Nonlinear Programming
    • Parallel computing for MINLP
  • Algorithm Studies
    • Heuristics for generalized branch and bound methods
    • Optimization Services based distributed parallel, e.g. communications, load balance handling.

Jun Ma, Sanjay Mehrotra and Huanyuan Sheng

Impact Solver for Optimization Services, November 8, 2006

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Impact GMIP Solver Features Algorithms

  • Starting Node

Jun Ma, Sanjay Mehrotra and Huanyuan Sheng

Impact Solver for Optimization Services, November 8, 2006

impact gmip solver features algorithms1
Impact GMIP Solver Features Algorithms
  • Subsequent Node

Jun Ma, Sanjay Mehrotra and Huanyuan Sheng

Impact Solver for Optimization Services, November 8, 2006

impact gmip solver features algorithms2
Impact GMIP Solver Features Algorithms
  • Generate Proper Branching Hyperplanes
    • Basis Reduction Based (Mehrotra and Li)
      • LLL
      • GBR (Generalized Basis Reduction)
    • Heuristics (ongoing)

Jun Ma, Sanjay Mehrotra and Huanyuan Sheng

Impact Solver for Optimization Services, November 8, 2006

slide8
Jun Ma, Sanjay Mehrotra and Huanyuan Sheng

Impact Solver for Optimization Services, November 8, 2006

slide9
Jun Ma, Sanjay Mehrotra and Huanyuan Sheng

Impact Solver for Optimization Services, November 8, 2006

impact gmip parallelization over distributed optimization services

Nodes (OSiL Integer)

Call back (OSrL)

Representation

OSiL - instance

OShL

OShL

OShL

OShL

OSoL - option

IMPACT

OSrL - result

OSpL - process

getJobID (String OSoL)

solve (String OSiL, String OSoL)

send (String OSiL, String OSoL)

retrieve (String OSoL)

kill (String OSoL)

knock (String OSpL, String OSoL)

IMPACT GMIP Parallelization over Distributed Optimization Services

ImpactGMIP

Lindo

OSServer

(Linux)

CPLEX

OSServer

(WinXP)

OSServer

(Mac OS)

Communication

OShL - hookup

Jun Ma, Sanjay Mehrotra and Huanyuan Sheng

Impact Solver for Optimization Services, November 8, 2006

conclusion
Conclusion
  • Introduced the Generalized MIP and showed it is friendliness for Parallelization
  • Showed Optimization Services has a general and high extendable design fit for many derived researches

Jun Ma, Sanjay Mehrotra and Huanyuan Sheng

Impact Solver for Optimization Services, November 8, 2006

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Jun Ma, Sanjay Mehrotra and Huanyuan Sheng

Impact Solver for Optimization Services, November 8, 2006