Approximating Min-Cost Chain-Constrained Spanning Trees: A Reduction from Weighted to Unweighted Problems

TL;DR

This paper introduces a polynomial-time algorithm for the min-cost chain-constrained spanning-tree problem that achieves constant-factor approximations for both cost and degree violations, using a novel reduction from weighted to unweighted problems via Lagrangian duality.

Contribution

It presents the first polynomial-time algorithm for min-cost chain-constrained spanning trees with bounded degree violations and cost approximation, employing a new reduction technique based on Lagrangian duality.

Findings

Achieves constant-factor approximation for cost and degree violations.

Introduces a reduction from weighted to unweighted problems using Lagrangian duality.

Provides improved algorithms for k-budgeted matroid basis problem.

Abstract

We study the {\em min-cost chain-constrained spanning-tree} (abbreviated \mcst) problem: find a min-cost spanning tree in a graph subject to degree constraints on a nested family of node sets. We devise the {\em first} polytime algorithm that finds a spanning tree that (i) violates the degree constraints by at most a constant factor {\em and} (ii) whose cost is within a constant factor of the optimum. Previously, only an algorithm for {\em unweighted} \cst was known \cite{olver}, which satisfied (i) but did not yield any cost bounds. This also yields the first result that obtains an $O(1)$-factor for {\em both} the cost approximation and violation of degree constraints for any spanning-tree problem with general degree bounds on node sets, where an edge participates in a super-constant number of degree constraints. A notable feature of our algorithm is that we {\em reduce} \mcst to unweighted \cst (and then utilize \cite{olver}) via a novel application of {\em Lagrangian duality} to simplify the {\em cost structure} of the underlying problem and obtain a decomposition into certain uniform-cost subproblems. We show that this Lagrangian-relaxation based idea is in fact applicable more generally and, for any cost-minimization problem with packing side-constraints, yields a reduction from the weighted to the unweighted problem. We believe that this reduction is of independent interest. As another application of our technique, we consider the {\em $k$-budgeted matroid basis} problem, where we build upon a recent rounding algorithm of \cite{BansalN16} to obtain an improved $n^{O(k^{1.5}/\epsilon)}$-time algorithm that returns a solution that satisfies (any) one of the budget constraints exactly and incurs a $(1+\epsilon)$-violation of the other budget constraints.