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cs.DS updates on arXiv.org

PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting Algorithms with Polynomially-Improved Approximation Factors for the $2 \rightarrow q$ Norm, and Applications A computational phase transition for learning-to-sample from Ising models Covering vertices by sequential stars Fermi-Dirac machines as quantizations of neurons A Comprehensive Evaluation of Vertex Elimination Algorithms for Algorithmic Differentiation A Tight Bound on Localization of Electrical Flows Optimal Dimension-Free Sampling for Regularized Classification Reducing the Randomness in Partition Oracles for Bounded Degree Minor-Free Graphs Beyond the Half-Approximation: Fair and Efficient Online Class Matching Efficient Uniform Sampling of Surjections via their Profiles Tractable Maximization of Budgeted Phylogenetic Diversity on Networks Utilizing Node Scanwidth Fairness in Aggregation: Optimal Top-$k$ and Improved Full Ranking Learning-Augmented Online Scheduling with Parsimonious Preemption Entropy Equivalence Testing Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees The Secretary Problem with a Stochastic Precursor Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification Block-Sphere Vector Quantization An Approximation Algorithm for Graph Label Selection Iterative Chow Filtering for Learning with Distribution Shift Complexity of Non-Log-Concave Sampling in Fisher Information Stochastic Matching via Local Sparsification Finite Sample Bounds for Learning with Score Matching What is Learnable in Valiant's Theory of the Learnable? Provable Quantization with Randomized Hadamard Transform Min-Max Optimization Requires Exponentially Many Queries Fast and Compact Graph Cuts for the Boykov-Kolmogorov Algorithm A proximal gradient algorithm for composite log-concave sampling
Discordant Compact Logic-Arithmetic Structures in Discret...
V. F. Romanov · 2013-09-24 · via cs.DS updates on arXiv.org

In sphere of research of discrete optimization algorithms efficiency the important place occupies a method of polynomial reducibility of some problems to others with use of special purpose components. In this paper a novel method of compact representation for sets of binary sequences in the form of "compact triplets structures" (CTS) and "compact couples structures" (CCS) is stated, supposing both logic and arithmetic interpretation of data. It is shown that any non-empty CTS in dual interpretation represents some unique Boolean formula in 3-CNF and the tabular CTS contains all satisfyig sets of the formula as concatenations of the triplets chosen from the neighbouring tiers. In general, any 3-CNF formula is transformed by decomposition to a system of discordant CTS's, each being associated with an individual permutation of variables constructed by a polynomial algorithm. As a result the problem of the formula satisfiability is reduced to the following one: ascertain the fact of existence (or absence) of a "joint satisfying set" (JSS) for all discordant structures, based on the different permutations. Further transformation of each CTS to CCS is used; correctness of preservation of the allowed sets is reached by simple algorithmic restrictions on triplets concatenation. Then the procedure of "inverting of the same name columns" in the various structures is entered for the purpose of reducing the problem of JSS revealing to elementary detection of n-tuples of zeros in the CCS system. The formula is synthesized, being on the structure a variation of 2-CNF, associated with the calculation procedure realizing adaptation of the polynomial algorithm of constraints distribution (well-known in the optimization theory) to the efficient resolving Boolean formula coded by means of discordant compact structures.