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Gilad Shmerler

Publications and source records attributed to Gilad Shmerler.

2 recordsLinked to original sources

Tight Approximation Results for Matroid Optimization with a Linear Constraint

We study the following class of matroid optimization problems with a linear constraint (P-MOL). Given a matroid M=(E,I), two weight functions $v,w:E\to R_{\ge 0}$, and a threshold $L\in R_{\ge 0}$, find $opt v(S)$ where S is either an independent set or a base of M satisfying a budget-type constraint: $w(S)\le L$ or $w(S)\ge L$, and $opt\in\{min,max\}$. P-MOL provides a unified representation for a broad family of NP-hard optimization problems, including budgeted matroid independent set, constrained minimum-basis, and knapsack-cover variants with a matroid constraint. Also, it naturally extends to multiple matroid constraints. In particular, we consider the matroid intersection cover (MIC) problem, where feasibility is defined by the common independent sets of two matroids and one seeks minimum $v(S)$ subject to $w(S)\ge L$. Our main result is a unified EPTAS for all nontrivial P-MOL variants, obtained by generalizing a technique of Hassin and Levin (SIAM J. Comput., 2004) for solving the constrained minimum spanning tree problem. Specifically, for any fixed $ε>0$, we present an algorithm running in time $|E|^{O(1)} (1/{ε^2})^{O(1/ε)}$ that outputs a feasible solution S whose value is at most $(1+ε)OPT$ for minimization variants and at least $(1-ε)OPT$ for maximization variants. This resolves the complexity status of all members of P-MOL, as none of these problems admits an FPTAS (Doron-Arad, Kulik and Shachnai, ICALP'24). Finally, we separate the P-MOL family from its extension to matroid intersection. We show that an EPTAS is unlikely to exist for the matroid intersection variant of P-MOL under a covering constraint, whereas an EPTAS is known to exist under a budget constraint. This highlights a qualitative difference between these two types of linear constraints that does not arise in the single-matroid setting.

cs.DS↗

An Algorithm-to-Contract Framework without Demand Queries

Consider costly and time-consuming tasks that add up to the success of a project, and must be fitted into a given time-frame. This is an instance of the classic budgeted maximization (knapsack) problem, which admits an FPTAS. Now assume an agent is performing these tasks on behalf of a principal, who is the one to reap the rewards if the project succeeds. The principal must design a contract to incentivize the agent. Is there still an approximation scheme? In this work we lay the foundations for an algorithm-to-contract framework, which transforms algorithms for combinatorial problems to handle contract design problems subject to the same combinatorial constraints. Our approach diverges from previous works in avoiding the assumption of demand oracle access. As an example, for budgeted maximization, we show how to "lift" the classic FPTAS to the best-possible (approximately-IC) FPTAS for the contract problem. We establish this through our local-to-global framework, in which the local step is to approximately solve a two-sided strengthened variant of the demand problem. The global step then utilizes the local one to find the approximately optimal contract. We apply our framework to a host of combinatorial constraints: multi-dimensional budgets, budgeted matroid, and budgeted matching constraints. In all cases we essentially match the best purely algorithmic approximation. Separately, we also develop a method for multi-agent contract settings. Our method yields the first approximation schemes for multi-agent contract settings that go beyond additive reward functions.

cs.GT↗