TY - GEN
T1 - Satisfiability to Coverage in Presence of Fairness, Matroid, and Global Constraints
AU - Inamdar, Tanmay
AU - Jain, Pallavi
AU - Lokshtanov, Daniel
AU - Sahu, Abhishek
AU - Saurabh, Saket
AU - Upasana, Anannya
N1 - Publisher Copyright:
© Tanmay Inamdar, Pallavi Jain, Daniel Lokshtanov, Abhishek Sahu, Saket Saurabh, and Anannya Upasana.
PY - 2024/7/1
Y1 - 2024/7/1
N2 - In the MaxSAT with Cardinality Constraint problem (CC-MaxSAT), we are given a CNF-formula Φ, and a positive integer k, and the goal is to find an assignment β with at most k variables set to true (also called a weight k-assignment) such that the number of clauses satisfied by β is maximized. Maximum Coverage can be seen as a special case of CC-MaxSat, where the formula Φ is monotone, i.e., does not contain any negative literals. CC-MaxSat and Maximum Coverage are extremely well-studied problems in the approximation algorithms as well as the parameterized complexity literature. Our first conceptual contribution is that CC-MaxSat and Maximum Coverage are equivalent to each other in the context of FPT-Approximation parameterized by k (here, the approximation is in terms of the number of clauses satisfied/elements covered). In particular, we give a randomized reduction from CC-MaxSat to Maximum Coverage running in time O(1/ϵ)k · (m + n)O(1) that preserves the approximation guarantee up to a factor of (1 − ϵ). Furthermore, this reduction also works in the presence of “fairness” constraints on the satisfied clauses, as well as matroid constraints on the set of variables that are assigned true. Here, the “fairness” constraints are modeled by partitioning the clauses of the formula Φ into r different colors, and the goal is to find an assignment that satisfies at least tj clauses of each color 1 ≤ j ≤ r. Armed with this reduction, we focus on designing FPT-Approximation schemes (FPT-ASes) for Maximum Coverage and its generalizations. Our algorithms are based on a novel combination of a variety of ideas, including a carefully designed probability distribution that exploits sparse coverage functions. These algorithms substantially generalize the results in Jain et al. [SODA 2023] for CC-MaxSat and Maximum Coverage for Kd,d-free set systems (i.e., no d sets share d elements), as well as a recent FPT-AS for Matroid Constrained Maximum Coverage by Sellier [ESA 2023] for frequency-d set systems.
AB - In the MaxSAT with Cardinality Constraint problem (CC-MaxSAT), we are given a CNF-formula Φ, and a positive integer k, and the goal is to find an assignment β with at most k variables set to true (also called a weight k-assignment) such that the number of clauses satisfied by β is maximized. Maximum Coverage can be seen as a special case of CC-MaxSat, where the formula Φ is monotone, i.e., does not contain any negative literals. CC-MaxSat and Maximum Coverage are extremely well-studied problems in the approximation algorithms as well as the parameterized complexity literature. Our first conceptual contribution is that CC-MaxSat and Maximum Coverage are equivalent to each other in the context of FPT-Approximation parameterized by k (here, the approximation is in terms of the number of clauses satisfied/elements covered). In particular, we give a randomized reduction from CC-MaxSat to Maximum Coverage running in time O(1/ϵ)k · (m + n)O(1) that preserves the approximation guarantee up to a factor of (1 − ϵ). Furthermore, this reduction also works in the presence of “fairness” constraints on the satisfied clauses, as well as matroid constraints on the set of variables that are assigned true. Here, the “fairness” constraints are modeled by partitioning the clauses of the formula Φ into r different colors, and the goal is to find an assignment that satisfies at least tj clauses of each color 1 ≤ j ≤ r. Armed with this reduction, we focus on designing FPT-Approximation schemes (FPT-ASes) for Maximum Coverage and its generalizations. Our algorithms are based on a novel combination of a variety of ideas, including a carefully designed probability distribution that exploits sparse coverage functions. These algorithms substantially generalize the results in Jain et al. [SODA 2023] for CC-MaxSat and Maximum Coverage for Kd,d-free set systems (i.e., no d sets share d elements), as well as a recent FPT-AS for Matroid Constrained Maximum Coverage by Sellier [ESA 2023] for frequency-d set systems.
KW - FPT Approximation
KW - Matroids
KW - Max SAT
KW - Partial Vertex Cover
UR - https://www.scopus.com/pages/publications/85198337799
U2 - 10.4230/LIPIcs.ICALP.2024.88
DO - 10.4230/LIPIcs.ICALP.2024.88
M3 - Conference contribution
AN - SCOPUS:85198337799
T3 - Leibniz International Proceedings in Informatics, LIPIcs
BT - 51st International Colloquium on Automata, Languages, and Programming, ICALP 2024
A2 - Bringmann, Karl
A2 - Grohe, Martin
A2 - Puppis, Gabriele
A2 - Svensson, Ola
PB - Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
T2 - 51st International Colloquium on Automata, Languages, and Programming, ICALP 2024
Y2 - 8 July 2024 through 12 July 2024
ER -