The field of algorithms and data structures is rapidly advancing, with a focus on developing efficient and scalable solutions to complex problems. Recent developments have led to significant improvements in areas such as matrix decomposition, numerical methods, and graph algorithms. The use of reinforcement learning and other optimization techniques has enabled the creation of more efficient and effective algorithms. Additionally, there has been a growing interest in developing algorithms that can handle large datasets and high-performance computing applications. Notable papers in this area include the development of efficient algorithms for the Hadamard decomposition and the application of reinforcement learning to mixed-precision numerical methods. Other notable works include the development of near-optimal hypergraph sparsification algorithms and improved streaming edge coloring algorithms. Overall, these advancements have the potential to impact a wide range of fields, from scientific computing to network analysis and machine learning.
Advancements in Efficient Algorithms and Data Structures
Sources
Efficient algorithms for the Hadamard decomposition
Mixed-Precision Conjugate Gradient Solvers with RL-Driven Precision Tuning
The Schur complements for $SDD_{1}$ matrices and their application to linear complementarity problems
A Note on the Complexity of Defensive Domination
Assessing the Performance of Mixed-Precision ILU(0)-Preconditioned Multiple-Precision Real and Complex Krylov Subspace Methods
AltGDmin: Alternating GD and Minimization for Partly-Decoupled (Federated) Optimization
Near-optimal Hypergraph Sparsification in Insertion-only and Bounded-deletion Streams
Multiplicative Spanners in Minor-Free Graphs
Improved Streaming Edge Coloring
From Theory to Practice: Engineering Approximation Algorithms for Dynamic Orientation
Graph modification of bounded size to minor-closed classes as fast as vertex deletion
Memory-efficient Sketch Acceleration for Handling Large Network Flows on FPGAs
Parallelizing the Approximate Minimum Degree Ordering Algorithm: Strategies and Evaluation
The Case for External Graph Sketching
Linear-Time Multilevel Graph Partitioning via Edge Sparsification
Pushing the frontiers of subexponential FPT time for Feedback Vertex Set