Approximation in (Poly-) Logarithmic Space [article]

Arindam Biswas and Venkatesh Raman and Saket Saurabh
2021 arXiv   pre-print
We develop new approximation algorithms for classical graph and set problems in the RAM model under space constraints. As one of our main results, we devise an algorithm for d-Hitting Set that runs in time n^O(d^2 + d/ϵ), uses O((d^2 + d/ϵ) log n) bits of space, and achieves an approximation ratio of O((d/ϵ) n^ϵ) for any positive ϵ≤1 and any natural number d. In particular, this yields a factor-O(log n) approximation algorithm which runs in time n^O(log n) and uses O(log^2 n) bits of space (for
more » ... constant d). As a corollary, we obtain similar bounds for Vertex Cover and several graph deletion problems. For bounded-multiplicity problem instances, one can do better. We devise a factor-2 approximation algorithm for Vertex Cover on graphs with maximum degree Δ, and an algorithm for computing maximal independent sets which both run in time n^O(Δ) and use O(Δlog n) bits of space. For the more general d-Hitting Set problem, we devise a factor-d approximation algorithm which runs in time n^O(d δ^2) and uses O(d δ^2 log n) bits of space on set families where each element appears in at most δsets. For Independent Set restricted to graphs with average degree d, we give a factor-(2d) approximation algorithm which runs in polynomial time and uses O(log n) bits of space. We also devise a factor-O(d^2) approximation algorithm for Dominating Set on d-degenerate graphs which runs in time n^O(log n) and uses O(log^2 n) bits of space. For d-regular graphs, we show how a known randomized factor-O(log d) approximation algorithm can be derandomized to run in time n^O(1) and use O(log n) bits of space. Our results use a combination of ideas from the theory of kernelization, distributed algorithms and randomized algorithms.