StringologyTimes

Data Structures and Algorithms: 2018/9/15-21

1: Multi-Modal Route Planning in Road and Transit Networks
2: Approximation algorithms for the three-machine proportionate mixed shop scheduling
3: Constant factor FPT approximation for capacitated k-median
4: Calculation of extended gcd by normalization
5: Equivalence between pathbreadth and strong pathbreadth
6: On the Reconstruction of Static and Dynamic Discrete Structures
7: Best-case and Worst-case Sparsifiability of Boolean CSPs
8: A Strongly Polynomial Algorithm for Linear Exchange Markets
9: Zeroth-order Nonconvex Stochastic Optimization: Handling Constraints, High-Dimensionality and Saddle-Points
10: On the Partition Set Cover Problem
11: Negative type diversities, a multi-dimensional analogue of negative type metrics
12: A Simple Approximation for a Hard Routing Problem
13: Utilizing Network Structure to Bound the Convergence Rate in Markov Chain Monte Carlo Algorithms
14: Connectivity and Structure in Large Networks
15: Optimal strategies for patrolling fences
16: Branch-and-bound for bi-objective integer programming
17: Finding k-Dissimilar Paths with Minimum Collective Length
18: Sublinear Time Low-Rank Approximation of Distance Matrices
19: Data-Driven Clustering via Parameterized Lloyd’s Families
20: Encoding two-dimensional range top-k queries
21: Shouji: A Fast and Efficient Pre-Alignment Filter for Sequence Alignment
22: The Warm-starting Sequential Selection Problem and its Multi-round Extension
23: Compressing and Indexing Aligned Readsets
24: Mean Estimation with Sub-Gaussian Rates in Polynomial Time
25: Local Density Estimation in High Dimensions
26: $L_1$ Shortest Path Queries in Simple Polygons
27: Sparsified SGD with Memory
28: Small Uncolored and Colored Choice Dictionaries
29: Simple Local Computation Algorithms for the General Lovasz Local Lemma
30: Compressed Sensing with Adversarial Sparse Noise via L1 Regression
31: Distributed coloring of graphs with an optimal number of colors
32: Data-compression for Parametrized Counting Problems on Sparse graphs