#approximation algorithms

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29 papers match

quant-ph2026

Approximate sampling from decoded quantum interferometry via Markov chain Monte Carlo methods

Elies Gil-Fuster, Matan Ninio, Lennart Bittel +4

The paper investigates whether classical Markov chain Monte Carlo methods, especially block‑Gibbs sampling, can reproduce the optimization performance of decoded quantum interferom…

#decoded quantum interferometry#markov chain monte carlo#optimization#max-xorsat
cs.CC2026

Optimal PSPACE-hardness of Approximating -CSP Reconfiguration

Shuichi Hirahara, Naoto Ohsaka

The paper proves that approximating the Maxmin q‑CSP Reconfiguration problem within a factor of 1/2^{q‑1}+ε is PSPACE‑hard for any q≥2, and shows that achieving a (1/2^{q‑1}‑ε)‑app…

#constraint satisfaction problems#reconfiguration#approximation algorithms#complexity theory
cs.DC2026

Safe Quotes for Retroactive Liquidity Pools

Peter Bro Miltersen

The paper examines how to compute safe price quotes for retroactive liquidity pools used in cross‑chain swaps, proves that approximating the optimal safe quote is NP‑hard, and offe…

#automated market makers#liquidity pools#retroactive pools#safe quotes
cs.CG2026

Tight UGC Thresholds for Geometric Stabbing Problems

Khaled Elbassioni, Rishikesh Gajjala, Saurabh Ray

The paper proves tight hardness thresholds under the Unique Games Conjecture for several geometric stabbing problems by linking integrality‑gap instances of covering LPs to matchin…

#geometric stabbing#unique games conjecture#hardness of approximation#lp rounding
cs.DS2026

Approximate Dual Separation for the Cluster LP: a 1.387 approximation for Correlation Clustering

David García-Soriano, Antoine Schohn

The paper presents a (1.3865+ε)-approximation algorithm for correlation clustering on complete graphs by introducing an efficient approximate dual separation oracle for the cluster…

#correlation clustering#approximation algorithms#linear programming#dual separation
cs.DS2026

Linear time approximation of the TV distance between product distributions

Konrad Anand, Alistair Benford, Heng Guo

The paper proposes a linear‑time algorithm that approximates the total variation distance between two product distributions.

#total variation distance#product distributions#approximation algorithms#linear time