Analysis of Two-variable Recurrence Relations with Application to Parameterized Approximations

In this paper we introduce randomized branching as a tool for parameterized approximation and develop the mathematical machinery for its analysis. Our algorithms improve the best known running times of parameterized approximation algorithms for Vertex Cover and $3$-Hitting Set for a wide range of approximation ratios. One notable example is a simple parameterized random $1.5$-approximation algorithm for Vertex Cover, whose running time of $O^*(1.01657^k)$ substantially improves the best known runnning time of $O^*(1.0883^k)$ [Brankovic and Fernau, 2013]. For $3$-Hitting Set we present a parameterized random $2$-approximation algorithm with running time of $O^*(1.0659^k)$, improving the best known $O^*(1.29^k)$ algorithm of [Brankovic and Fernau, 2012]. The running times of our algorithms are derived from an asymptotic analysis of a wide class of two-variable recurrence relations of the form: $$p(b,k) = \min_{1\leq j \leq N} \sum_{i=1}^{r_j} \bar{\gamma}_i^j \cdot p(b-\bar{b}^j_i, k-\bar{k}_i^j),$$ where $\bar{b}^j$ and $\bar{k}^j$ are vectors of natural numbers, and $\bar{\gamma}^j$ is a probability distribution over $r_j$ elements, for $1\leq j \leq N$. Our main theorem asserts that for any $\alpha>0$, $$\lim_{k \rightarrow \infty } \frac{1}{k} \log p(\alpha k,k) = -\max_{1\leq j \leq N} M_j,$$ where $M_j$ depends only on $\alpha$, $\bar{\gamma}^j$, $\bar{b}^j$ and $\bar{k}^j$, and can be efficiently calculated by solving a simple numerical optimization problem. To this end, we show an equivalence between the recurrence and a stochastic process. We analyze this process using the Method of Types, by introducing an adaptation of Sanov's theorem to our setting. We believe our novel analysis of recurrence relations which is of independent interest is a main contribution of this paper