Frameworks for Designing In-place Graph Algorithms
Sankardeep Chakraborty, Anish Mukherjee, Venkatesh Raman, Srinivasa, Rao Satti

TL;DR
This paper introduces relaxed read-only memory models that enable more space-efficient graph algorithms like BFS and DFS, achieving significant reductions in space complexity while maintaining polynomial time performance.
Contribution
The authors propose and analyze relaxed ROM models allowing in-place modifications of adjacency lists, leading to space-efficient graph algorithms with practical potential.
Findings
DFS and BFS can be implemented with O(log n) bits of extra space.
Reachability and shortest path can be computed with reduced space.
Models are more powerful than classical ROM if L ≠ P.
Abstract
Read-only memory model is a classical model of computation to study time-space tradeoffs of algorithms. One of the classical results on the ROM model is that any sorting algorithm that uses O(s) words of extra space requires comparisons for and the bound has also been recently matched by an algorithm. However, if we relax the model (from ROM), we do have sorting algorithms (say Heapsort) that can sort using comparisons using bits of extra space, even keeping a permutation of the given input sequence at any point of time during the algorithm. We address similar questions for graph algorithms. We show that a simple natural relaxation of ROM model allows us to implement fundamental graph search methods like BFS and DFS more space efficiently than in ROM. By simply allowing elements in the adjacency list of a vertex to…
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