Backdoor Decomposable Monotone Circuits and Propagation Complete Encodings

Authors: Petr Kučera, Petr Savický3832-3840

AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical The main result of our paper is that a PC-BDMC can be compiled into a PC encoding of size polynomial with respect to the total size of the input BDMC. As a consequence, we get that both PC-BDMCs and PC encodings share the same algorithmic properties while being equally succinct. Theorem 1. Let D be a smooth PC-BDMC representing a function f(x). Then we can construct in polynomial time a PC encoding of f(x). Theorem 3. PC-BDMCs (and thus also PC encodings) are strictly more succinct than generalized PC backdoor trees. Proof. For a given n, let us consider three sets of variables: x = {xi,j | i, j {1, . . . , n}}, y = {yi,j,k | i, j {1, . . . , n}, k {1, . . . , n 1}}, and z = {zi,j | i, j {1, . . . , n}}. For every i = 1, . . . , n, we introduce formulas...
Researcher Affiliation Academia Petr Kuˇcera,1 Petr Savick y2 1 Department of Theoretical Computer Science and Mathematical Logic, Faculty of Mathematics and Physics, Charles University, Czech Republic 2 Institute of Computer Science of the Czech Academy of Sciences, Czech Republic
Pseudocode No The paper defines logical constructions and presents clauses in Table 1, but it does not contain structured pseudocode or algorithm blocks.
Open Source Code No The paper does not provide any specific links to source code repositories or explicit statements about code availability for the described methodology.
Open Datasets No The paper is theoretical and does not involve empirical evaluation on datasets, so there is no mention of publicly available datasets for training.
Dataset Splits No As a theoretical paper, it does not describe experimental procedures involving dataset splits for training, validation, or testing.
Hardware Specification No The paper is theoretical and does not describe experimental procedures that would require specific hardware specifications.
Software Dependencies No The paper focuses on theoretical concepts and does not list any specific software dependencies with version numbers required for experiments.
Experiment Setup No The paper is theoretical and does not include details about an experimental setup, such as hyperparameters or training configurations.