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Piecewise parametric structure in the pooling problem: from sparse strongly-polynomial solutions to NP-hardness.

Radu Baltean-Lugojan1, Ruth Misener1

  • 1Department of Computing, Imperial College London, 180 Queens Gate, London, SW7 2AZ UK.

Journal of Global Optimization : an International Journal Dealing with Theoretical and Computational Aspects of Seeking Global Optima and Their Applications in Science, Management and Engineering
|April 9, 2019
PubMed
Summary

This study reveals the piecewise structure of standard pooling problems, a complex optimization challenge in engineering. Uncovering topological sparsity clarifies the problem

Keywords:
DiscretizationGlobal optimizationPiecewise structureP / NP boundarySparsityStandard pooling problemStrongly-polynomial algorithms

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Area of Science:

  • Process Systems Engineering
  • Optimization Theory
  • Computational Complexity

Background:

  • The standard pooling problem is a NP-hard, non-convex optimization problem frequently encountered in process systems engineering.
  • Existing approaches often struggle with the inherent complexity and non-convexity of these problems.

Purpose of the Study:

  • To investigate the topological structure and sparsity of the single quality standard pooling problem using a parametric approach.
  • To validate the hypothesis that pooling problems are fundamentally based on piecewise-defined functions.
  • To establish conditions under which the problem's complexity relates to the P/NP boundary.

Main Methods:

  • A parametric approach is employed to analyze the pooling problem in its p-formulation.
  • Dominant active topologies are introduced under relaxed flow availability to identify sparsity.
  • The association between sparse patterns and piecewise objective functions is mathematically demonstrated.

Main Results:

  • The analysis confirms that pooling problems exhibit a piecewise structure, aligning with prior intuition.
  • Explicit identification of sparsity in pooling problems is achieved through dominant active topologies.
  • Conditions for the vanishing of sparsity and the emergence of combinatorial complexity (P/NP boundary) are elucidated.

Conclusions:

  • The parametric approach effectively reveals the underlying piecewise nature and sparsity of standard pooling problems.
  • Understanding sparsity is crucial for managing the computational complexity of these optimization problems.
  • The findings provide a theoretical foundation for developing more efficient algorithms for pooling problems.