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U. Khan

26 papers hereh-index 364.9k citations136 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author11
  • last author15

Across the 26 of 26 papers where every author was matched, so the position is known.

fields
  • math.OC13
  • cs.LG6
  • eess.SY6
  • eess.SP1
same name
  • U. Khan — 4 papers, h 33
  • U. Khan — 2 papers, h 6
  • U. Khan — 1 paper
  • U. Khan — 1 paper, h 3
  • U. Khan — 1 paper, h 1
  • U. Khan — 1 paper, h 4

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20172022
most citedGradient tracking and variance reduction for decentralized optimization and machine learning

7 citations · 17 across the 11 of their papers we have counts for

collaborators
Showing 2018Show all

4 papers · 1 filter

math.OC2018

Optimization over time-varying directed graphs with row and column-stochastic matrices

Fakhteh Saadatniaki, Ran Xin, Usman A. Khan

In this paper, we provide a distributed optimization algorithm, termed as TV-AB, that minimizes a sum of convex functions over time-varying, random directed graphs. Con…

math.OC2018

Distributed heavy-ball: A generalization and acceleration of first-order methods with gradient tracking

Ran Xin, Usman A. Khan

We study distributed optimization to minimize a global objective that is a sum of smooth and strongly-convex local cost functions. Recently, several algorithms over undirected and…

math.OC2018

FROST -- Fast row-stochastic optimization with uncoordinated step-sizes

Ran Xin, Chenguang Xi, Usman A. Khan

In this paper, we discuss distributed optimization over directed graphs, where doubly-stochastic weights cannot be constructed. Most of the existing algorithms overcome this issue…

math.OC2018

A linear algorithm for optimization over directed graphs with geometric convergence

Ran Xin, Usman A. Khan

In this letter, we study distributed optimization, where a network of agents, abstracted as a directed graph, collaborates to minimize the average of locally-known convex functions…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.