Advances in Graph-Based Methods and Optimization Techniques

The field of graph-based methods and optimization techniques is rapidly advancing, with a focus on developing more efficient and effective algorithms for solving complex problems. Recent developments have centered around improving the performance of graph-based approximate nearest neighbor search methods, optimizing the layout of railroad diagrams, and analyzing the structural parameterizations of graph-based problems. Additionally, there have been significant advancements in the development of new algorithms for solving classic problems such as the Maximum Cut problem and the 2-Edge-Connected Spanning Subgraph problem. Noteworthy papers include the proposal of a novel and principled method for selecting the truncation parameter in Sparse Neighborhood Graphs, which achieves comparable or superior performance in terms of query latency and Recall@10 compared to commonly used binary search heuristics. Another notable paper presents a scalable framework for solving the Maximum Cut problem using projected gradient ascent on quadratic objectives, which achieves comparable or superior performance relative to recent training-data-intensive and dataless approaches.

Sources

Graph-Based Approximate Nearest Neighbor Search Revisited: Theoretical Analysis and Optimization

Automatic layout of railroad diagrams

On the Structural Parameterizations of 2-Club with Triangle Constraints

Constant time enumeration of perfect bipartite matchings

Analyzing and improving a classical Betti number estimation algorithm

Comparative Analysis of FOLD-SE vs. FOLD-R++ in Binary Classification and XGBoost in Multi-Category Classification

A Layered Implementation Framework for Regular Languages

A Scalable Lift-and-Project Differentiable Approach For the Maximum Cut Problem

New constructions of cyclic constant-dimension subspace codes based on Sidon spaces and subspace polynomials

Graph-based Clustering Revisited: A Relaxation of Kernel $k$-Means Perspective

GraphBLAS Mathematical Opportunities: Parallel Hypersparse, Matrix Based Graph Streaming, and Complex-Index Matrices

Optimization of Base-n Radix Sort for Skewed Datasets

Gate-Based and Annealing-Based Quantum Algorithms for the Maximum K-Plex Problem

Multi-population Ensemble Genetic Programming via Cooperative Coevolution and Multi-view Learning for Classification

A Better-Than-$5/4$-Approximation for Two-Edge Connectivity

SS-GUMAP, SL-GUMAP, SSSL-GUMAP: Fast UMAP Algorithms for Large Graph Drawing

BH-tsNET, FIt-tsNET, L-tsNET: Fast tsNET Algorithms for Large Graph Drawing

Fully Tensorized GPU-accelerated Multi-population Evolutionary Algorithm for Constrained Multiobjective Optimization Problems

Understanding the ratio of the partition sum to its Bethe approximation via double covers

Output-Sensitive Evaluation of Acyclic Conjunctive Regular Path Queries

Testable algorithms for approximately counting edges and triangles in sublinear time and space

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