inzicht - Parameterized Complexity - # Equivalence of Fine-Grained Problems

Primal Pathwidth SETH: Equivalence and Complexity Hypothesis

Q: What implications does the Primal Pathwidth SETH have on counting versions of connectivity problems

Primal Pathwidth SETH has significant implications on counting versions of connectivity problems. The hypothesis asserts that for all ε > 0, there is no algorithm capable of deciding if a given CNF formula is satisfiable in time (2 - ε)pwnO(1), where pw represents the pathwidth of the primal graph. When considering counting versions of connectivity problems, such as determining the number of satisfying assignments for a CNF formula based on its primal pathwidth, falsifying the PP-SETH would imply potential improvements in algorithm complexity. This could lead to advancements in solving counting and parity versions of connectivity problems efficiently within the specified constraints.

Q: How does the PP_SETH hypothesis compare to the parity version regarding improvements in algorithm complexity

In comparison to the standard Primal Pathwidth SETH hypothesis, which focuses on decision-making algorithms for SAT instances with specific time complexities relative to pathwidth, the parity version introduces considerations related to determining whether the number of satisfying assignments is even or odd. When evaluating improvements in algorithm complexity under both hypotheses, it becomes essential to address not only decision-based challenges but also nuances associated with parity calculations. Falsifying either version may result in enhanced algorithms for addressing these specialized requirements within parameterized complexity settings.

Q: What challenges may arise when applying the PP_SETH hypothesis to Cut&Count technique associated problems

Applying the PP_SETH hypothesis to Cut&Count technique-related problems may present several challenges due to their inherent complexities and reliance on advanced computational methods like fast subset convolution. These techniques often involve intricate processes beyond simple dynamic programming over linear structures like path decompositions. Therefore, adapting Cut&Count methodology to align with PP_SETH assumptions might require innovative approaches that can effectively handle more sophisticated computations involved in these problem-solving strategies. Ensuring that reductions maintain accuracy while accommodating intricate details specific to Cut&Count applications will be crucial when exploring this intersection between hypothesis and practical implementation scenarios.

Belangrijkste concepten

Primal Pathwidth-Strong Exponential Time Hypothesis (PP-SETH) unifies fine-grained questions in parameterized complexity, showing equivalence and sharpening lower bounds.

Samenvatting

Standalone Note here

Introduction:

Dynamic Programming (DP) importance in parameterized complexity.
Central role of DP in NP-hard graph problems with graph width parameters.
Interest in optimal algorithms and known results for Coloring problem.

Robustness:

PP-SETH equivalence to various SAT variants and CSPs with non-binary alphabets.
Importance of promise CSP for reductions.

Supporting Evidence:

PP-SETH implication by SETH, k-OVA, and Set Cover Conjecture.
Strengthening confidence in lower bounds under more plausible assumptions.

Single-exponential FPT problems:

Equivalence of various problems to falsifying the PP-SETH.
Implications for k-Coloring, Independent Set, Dominating Set, and Set Cover.

Super-exponential FPT problems:

Equivalence of Coloring and C4-Hitting Set to PP-SETH falsification.

XNLP-complete problems:

Equivalence of List Coloring, DFA Intersection, and Independent Set Reconfiguration to PP-SETH falsification.

Overview of techniques:

Importance of robustness in formulations for reductions.

Previous work:

Comparison with ETH-based bounds limitations due to one-way reductions.

Discussion and Directions for Further Work:

Potential investigation into treewidth variant of the PP-SETH hypothesis.

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3-SAT cannot be solved in time (2 − ε)pwnO(1)
(k − ε)pwnO(1) algorithm for k-coloring is optimal under SETH
List Coloring can be solved in time n(1−ε)pw under PP_SETH

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Belangrijkste Inzichten Gedestilleerd Uit

The Primal Pathwidth SETH

by Michael Lamp... om arxiv.org 03-13-2024

https://arxiv.org/pdf/2403.07239.pdf

Diepere vragen

What implications does the Primal Pathwidth SETH have on counting versions of connectivity problems

Primal Pathwidth SETH has significant implications on counting versions of connectivity problems. The hypothesis asserts that for all ε > 0, there is no algorithm capable of deciding if a given CNF formula is satisfiable in time (2 - ε)pwnO(1), where pw represents the pathwidth of the primal graph. When considering counting versions of connectivity problems, such as determining the number of satisfying assignments for a CNF formula based on its primal pathwidth, falsifying the PP-SETH would imply potential improvements in algorithm complexity. This could lead to advancements in solving counting and parity versions of connectivity problems efficiently within the specified constraints.

How does the PP_SETH hypothesis compare to the parity version regarding improvements in algorithm complexity

In comparison to the standard Primal Pathwidth SETH hypothesis, which focuses on decision-making algorithms for SAT instances with specific time complexities relative to pathwidth, the parity version introduces considerations related to determining whether the number of satisfying assignments is even or odd. When evaluating improvements in algorithm complexity under both hypotheses, it becomes essential to address not only decision-based challenges but also nuances associated with parity calculations. Falsifying either version may result in enhanced algorithms for addressing these specialized requirements within parameterized complexity settings.

What challenges may arise when applying the PP_SETH hypothesis to Cut&Count technique associated problems

Applying the PP_SETH hypothesis to Cut&Count technique-related problems may present several challenges due to their inherent complexities and reliance on advanced computational methods like fast subset convolution. These techniques often involve intricate processes beyond simple dynamic programming over linear structures like path decompositions. Therefore, adapting Cut&Count methodology to align with PP_SETH assumptions might require innovative approaches that can effectively handle more sophisticated computations involved in these problem-solving strategies. Ensuring that reductions maintain accuracy while accommodating intricate details specific to Cut&Count applications will be crucial when exploring this intersection between hypothesis and practical implementation scenarios.