Worst and Best Irredundant Sum-of-Products Expressions

Abstract

In an irredundant sum-of-products expression (ISOP), each product is a prime implicant (PI) and no product can be deleted without changing the function. Among the ISOPs for some function f, a worst ISOP (WSOP) is an ISOP with the largest number of PIs and a minimum ISOP (MSOP) is one with the smallest number. We show a class of functions for which the Minato-Morreale ISOP algorithm produces WSOPs. Since the ratio of the size of the WSOP to the size of the MSOP is arbitrarily large when n, the number of variables, is unbounded, the Minato-Morreale algorithm can produce results that are very far from minimum. We present a class of multiple-output functions whose WSOP size is also much larger than its MSOP size. For a set of benchmark functions, we show the distribution of ISOPs to the number of PIs. Among this set are functions where the MSOPs have almost as many PIs as do the WSOPs. These functions are known to be easy to minimize. Also, there are benchmark functions where the fraction of ISOPs that are MSOPs is small and MSOPs have many fewer PIs than the WSOPs. Such functions are known to be hard to minimize. For one class of functions, we show that the fraction of ISOPs that are MSOPs approaches 0 as n approaches infinity, suggesting that such functions are hard to minimize.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2001
Accession Number
ADA605401

Entities

People

  • Jon T. Butler
  • Tsutomu Sasao

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Computer Science
  • Computers
  • Coverings
  • Education
  • Electronic Mail
  • Information Operations
  • Mathematics
  • Monotone Functions
  • Numbers
  • Observation
  • Permutations
  • Redundancy
  • Scientific Research
  • Square Roots
  • Theorems

Fields of Study

  • Computer science

Readers

  • Analytical Mechanics
  • Graph Algorithms and Convex Optimization.