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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
Variações do Problema de Distância de Rearranjos
Alexsandro Oliveira Alexandrino · 2024-04-28 · via cs.DS updates on arXiv.org

Considering a pair of genomes, the goal of rearrangement distance problems is to estimate how distant these genomes are from each other based on genome rearrangements. Seminal works in genome rearrangements assumed that both genomes being compared have the same set of genes (balanced genomes) and, furthermore, only the relative order of genes and their orientations, when they are known, are used in the mathematical representation of the genomes. In this case, the genomes are represented as permutations, originating the Sorting Permutations by Rearrangements problems. The main problems of Sorting Permutations by Rearrangements considered DCJs, reversals, transpositions, or the combination of both reversals and transpositions, and these problems have their complexity known. Besides these problems, other ones were studied involving the combination of transpositions with one or more of the following rearrangements: transreversals, revrevs, and reversals. Although there are approximation results for these problems, their complexity remained open. Some of the results of this thesis are the complexity proofs for these problems. Furthermore, we present a new 1.375-approximation algorithm, which has better time complexity, for the Sorting Permutations by Transpositions. When considering unbalanced genomes, it is necessary to use insertions and deletions to transform one genome into another. In this thesis, we studied Rearrangement Distance problems on unbalanced genomes considering only gene order and their orientations (when they are known), as well as Intergenic Rearrangement Distance problems, which incorporate the information regarding the size distribution of intergenic regions, besides the use of gene order and their orientations (when they are known). We present complexity proofs and approximation algorithms for problems that include reversals and transpositions.