SearcharxivSearch

arXiv · 2503.16336

A parallel algorithm for the odd two-face shortest k-disjoint path problem

Abstract

The shortest Disjoint Path problem (SDPP) requires us to find pairwise vertex disjoint paths between k designated pairs of terminal vertices such that the sum of the path lengths is minimum. The focus here is on SDPP restricted to planar graphs where all terminals are arbitrarily partitioned over two distinct faces with the additional restriction that each face is required to contain an odd number of terminals. We call this problem the Odd two-face planar SDPP. It is shown that this problem is solvable in randomized polynomial time and even in RNC. This is the first parallel (or even polynomial time) solution for the problem. Our algorithm combines ideas from the randomized solution for 2-SDPP by Bj\"orklund and Huslfeldt with its parallelization by Datta and Jaiswal along with the deterministic algorithm for One-face planar SDPP by Datta, Iyer, Kulkarni and Mukherjee. The proof uses a combination of two involutions to reduce a system of linear equations modulo a power of 2 to a system of triangular form that is, therefore, invertible. This, in turn, is proved by showing that the matrix of the equations, can be interpreted as (the adjacency matrix of) a directed acyclic graph (DAG). While our algorithm is primarily algebraic the proof remains combinatorial. We also give a parallel algorithm for the (A + B)-SDPP introduced by Hirai and Namba.

Explore related subjects

Keep this discovery

BibTeXRIS

Srijan Chakraborty, Samir Datta. 2025-03-20. A parallel algorithm for the odd two-face shortest k-disjoint path problem. https://arxiv.org/abs/2503.16336

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Quasi-Monte Carlo Beyond Hardy-Krause II: $(1 + \varepsilon)n$ Samples Suffice

Numerical integration studies how well one can estimate the integral of a function $f$ over $[0,1)^d$ using $n$ sample points. The two classical methods, Monte Carlo (MC) and quasi-Monte Carlo (QMC), have complementary strengths and weaknesses, and a fundamental question is to design an approach that combines the benefits of both. Recently, building on the transference principle in discrepancy theory, Bansal and Jiang~\cite{BJ25a} gave a randomized QMC method that bridges MC and QMC guarantees using only i.i.d.\ samples. Their method also goes beyond the classical Koksma--Hlawka inequality: it achieves integration error $\widetilde{O}_d(\sigma_{\mathsf{SO}}(f)/n)$, where the smoothed-out variation $\sigma_{\mathsf{SO}}(f)$ can be substantially smaller than the Hardy--Krause variation that governs the classical bound. However, their algorithm requires $n^2$ i.i.d.\ samples as input, and this quadratic blowup is inherent to any method based on the transference principle. In this work, we bypass the quadratic blowup: for any constant $\varepsilon > 0$, we show that $(1+\varepsilon)n$ i.i.d.\ samples suffice to both obtain the beyond-Hardy--Krause guarantee of~\cite{BJ25a}, resolving an open problem posed there, and to produce low-discrepancy point sequences. Our algorithms are variants of the online Haar-thinning method of Dwivedi, Feldheim, Gurel-Gurevich, and Ramdas~\cite{DFG+19}.

cs.DS

Single-Exponential Algorithms and a Polynomial Kernel for Strong Connectivity Augmentation

Strong Connectivity Augmentation (SCA) asks whether a directed acyclic graph can be made strongly connected by adding at most $k$ prescribed links whose total weight is within a given budget. Klinkby, Misra, and Saurabh (SODA 2021) gave an $O^*(2^{O(k\log k)})$-time algorithm and asked whether the problem admits a single-exponential parameterized algorithm and a polynomial kernel. We answer both questions affirmatively: SCA can be solved in $O^*(9^k)$ time and admits a polynomial kernel with $O(k^4)$ vertices and $O(k^{16})$ bits. For unweighted SCA, we obtain $O^*(4^k)$ time and a kernel with $O(k^3)$ vertices. Our algorithms are based on a particularly simple reduction to Strongly Connected Spanning Subgraph with two edge costs.

cs.DS