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Found 7 papers, 0 papers with code
Top-$K$ ranking with a monotone adversary
On the analytical front, we provide a refined $\ell_\infty$ error analysis of the weighted MLE that is more explicit and tighter than existing analyses.
Conjugate Gradients and Accelerated Methods Unified: The Approximate Duality Gap View
This note provides a novel, simple analysis of the method of conjugate gradients for the minimization of convex quadratic functions.
Alternating Randomized Block Coordinate Descent
While various block-coordinate-descent-type methods have been studied extensively, only alternating minimization – which applies to the setting of only two blocks – is known to have convergence time that scales independently of the least smooth block.
Connected Subgraph Detection with Mirror Descent on SDPs
We propose a novel, computationally efficient mirror-descent based optimization framework for subgraph detection in graph-structured data.
Spectral Sparsification and Regret Minimization Beyond Matrix Multiplicative Updates
In this paper, we provide a novel construction of the linear-sized spectral sparsifiers of Batson, Spielman and Srivastava [BSS14].
Linear Coupling: An Ultimate Unification of Gradient and Mirror Descent
First-order methods play a central role in large-scale machine learning.
Flow-Based Algorithms for Local Graph Clustering
A very elegant algorithm for this problem has been given by Andersen and Lang [AL08] and requires solving a small number of single-commodity maximum flow computations over the whole graph G. In this paper, we introduce LocalImprove, the first cut-improvement algorithm that is local, i. e. that runs in time dependent on the size of the input set A rather than on the size of the entire graph.
