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Hardness studies for distributed systems

Hardness studies for distributed systems. Patrick De Causmaecker Stefaan Haspeslagh Tommy Messelis. Empirical Hardness Indicators. What is empirical hardness? empirical: performance of some algorithm hardness: measured by some performance criteria

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Hardness studies for distributed systems

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  1. Hardness studies for distributed systems Patrick De Causmaecker StefaanHaspeslagh Tommy Messelis

  2. Empirical Hardness Indicators P. De Causmaecker, S. Haspeslagh, T. Messelis • What is empirical hardness? empirical: performance of some algorithm hardness: measured by some performance criteria • Time spent by an algorithm searching for a solution • Quality of (optimal) solution • Gap between optimal and found solution • Prediction based on efficiently computable features of the instance at hand • E.g. clauses-to-variables ratio of uniform random 3SAT problems

  3. Key idea P. De Causmaecker, S. Haspeslagh, T. Messelis • Build models that predict the empirical hardness of an instance • Know in advance what to expect from a given instance • Algorithm portfolios • Because there is no single best algorithm for all instances of a given distribution • Automated parameter tuning • General procedure first introduced by Leyton-Brown et al. K. Leyton-Brown, E. Nudelman, Y. Shoham. Learning the EmpiricalHardness of OptimizationProblems: The Case of CombinatorialAuctions. In: LectureNotes in Computer Science, 2002.

  4. “Leyton-Brown procedure” P. De Causmaecker, S. Haspeslagh, T. Messelis • Select a problem instance distribution • Define a set of algorithms • Come up with a set of inexpensive, distribution independent features • Generate an instance set. Calculate features and determine algorithm performances • Eliminate correlated & uninformative features • Use machine learning techniques to select a function of the features that predicts algoritm performance for all algoritms

  5. Does it work? P. De Causmaecker, S. Haspeslagh, T. Messelis Yes it does! • Winner determination problem for combinatorial auctions. • Propositional Satisfiability Problems • SATzilla • Our new approach: Nurse Rostering • Still on its way

  6. Hardness studies for DICOMAS P. De Causmaecker, S. Haspeslagh, T. Messelis • Dicomas: • 2 application domains: • Supply Chain Management • eHealth • Similar hardness studies needed

  7. Supply Chain Management K1 K2 K3 Kx 4PL Fixed contract 3PL Temporary 3PL P. De Causmaecker, S. Haspeslagh, T. Messelis

  8. Optimisation K1 K2 K3 Kx 4PL Fixed contract 3PL Temporary 3PL P. De Causmaecker, S. Haspeslagh, T. Messelis • Optimisation: • Costs: • Fixed contracts • Capacity • Temporary contracts

  9. Research questions P. De Causmaecker, S. Haspeslagh, T. Messelis • Input from other partners: • Solution methods to tackle SCM/eHealth problem • Question: • What is the performance of these methods • How? • Simulation • Determine properties of the problem (instances) for a certain method • See our work for non distributed methods

  10. Research questions P. De Causmaecker, S. Haspeslagh, T. Messelis • Online monitoring: • research for parameters that can identify and might predict problem situations and/or critical points • E.g. bullwhip effect

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