Packing Linear Programs and Fractional Knapsack using Comparison Oracles

Ritabrata Barat; Siddharth Barman; Nirjhar Das; Sukruta Midigeshi

Home/Archive/arXiv

arXiv · 2607.19557

Packing Linear Programs and Fractional Knapsack using Comparison Oracles

Ritabrata Barat·Siddharth Barman·Nirjhar Das·Sukruta Midigeshi

View original source ↗Read source document ↗Export metadata ↓

Abstract

We study the problem of recovering the objective of a packing linear program when the algorithm accesses only comparison information about optimal solutions under varying constraint matrices. Motivated by optimization with comparison oracles (Cohen-Addad et al., STOC 2026) and preference feedback (Kaufmann et al., TMLR 2025), this strengthens inverse-optimization frameworks by replacing direct observations of optimal solutions with ordinal queries. We focus on the fractional knapsack problem, where the packing linear program (LP) has a single budget constraint specified by item prices, and the objective is determined by item values. This captures monopoly-pricing where a seller infers a buyer's unknown valuations for divisible items from comparison information. The algorithm queries an oracle with two price vectors, returning which optimal solution has the larger total packing or objective value. Such oracles abstract discrete-choice surveys of buyers choosing between differently priced alternatives. For fractional knapsack, we develop a polynomial-time algorithm recovering item values up to scale using $O(n \log(1/\delta)+B^2)$ comparison queries, where $n$ is the number of items, $B$ is the knapsack capacity, and $\delta$ is the value grid resolution. We complement this with an $\Omega(n \log(1/\delta))$ lower bound. A key insight is that in the comparison-oracle model, fractional knapsack is as general as packing LPs. Our algorithm solves the packing setting by treating a constraint matrix row as the price vector and zeroing the rest. The $\Omega(n \log(1/\delta))$ lower bound continues to hold for packing LPs, making our upper bound essentially best possible, up to a linear-factor gap. Finally, we extend our algorithm to profit-maximization, yielding a comparison-oracle analogue of the revealed-preference result of Amin et al. (AAAI 2015).

Explore related subjects

cs.DS cs.GT

Keep this discovery

BibTeX RIS

Ritabrata Barat, Siddharth Barman, Nirjhar Das, Sukruta Midigeshi. 2026-07-21. Packing Linear Programs and Fractional Knapsack using Comparison Oracles. https://arxiv.org/abs/2607.19557

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

KEEP EXPLORING

Related papers

arXiv2026

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↗

arXiv2026

A deterministic $(1+\varepsilon)^n$ approximation for the permanent of a nonnegative matrix

For every fixed $0<\varepsilon\le1$, we give a deterministic strongly polynomial algorithm that, given a nonnegative matrix $A\in\mathbb{R}_{\ge0}^{n\times n}$, returns $Q$ satisfying $\operatorname{per} A\le Q\le(1+\varepsilon)^n\operatorname{per} A$.

cs.DS↗

arXiv2026

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↗