Improved space-time tradeoff for TSP via extremal set systems
Justin Dallant, L\'aszl\'o Kozma

TL;DR
This paper presents a new space-time tradeoff for solving the Traveling Salesman Problem (TSP) that improves upon previous bounds for all relevant T values, using extremal set systems and combinatorial constructions.
Contribution
It introduces a novel tradeoff for TSP solving that surpasses all prior bounds for 2 < T < 4, utilizing extremal combinatorics and disproving a related conjecture.
Findings
Achieves a minimum of ST < 3.572 for TSP solving tradeoff.
Constructs sparse set systems with many maximal chains, of independent combinatorial interest.
Extends techniques to permutation problems over arbitrary semirings, improving space-time tradeoffs.
Abstract
The traveling salesman problem (TSP) is a cornerstone of combinatorial optimization and has deeply influenced the development of algorithmic techniques in both exact and approximate settings. Yet, improving on the decades-old bounds for solving TSP exactly remains elusive: the dynamic program of Bellman, Held, and Karp from 1962 uses time and space, and the divide-and-conquer approach of Gurevich and Shelah from 1987 uses time and polynomial space. A straightforward combination of the two algorithms trades off time and space at various points of the curve . An improvement to this tradeoff when was found by Koivisto and Parviainen (SODA 2010), yielding a minimum of . Koivisto and Parviainen show their method to be optimal among a broad class of partial-order-based approaches,…
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