Topological Parameters for Time-Space Tradeoff

TL;DR

This paper introduces a family of algorithms that balance space and time in reasoning and optimization tasks by combining tree-clustering with conditioning, enabling tailored solutions based on problem structure.

Contribution

It presents a novel approach to trade space for time in algorithms, allowing selection of optimal algorithms based on specific resource constraints.

Findings

Algorithms effectively trade space for time in probabilistic and deterministic reasoning.

Analysis of problem structure guides optimal algorithm selection.

Flexible spectrum of algorithms tailored to resource specifications.

Abstract

In this paper we propose a family of algorithms combining tree-clustering with conditioning that trade space for time. Such algorithms are useful for reasoning in probabilistic and deterministic networks as well as for accomplishing optimization tasks. By analyzing the problem structure it will be possible to select from a spectrum the algorithm that best meets a given time-space specification.