activity
20172019
most citedThe Approximate Duality Gap Technique: A Unified Theory of First-Order Methods

58 citations · 121 across the 6 of their papers we have counts for

collaborators

7 papers

math.OC2019★ 2 cited

Conjugate Gradients and Accelerated Methods Unified: The Approximate Duality Gap View

Jelena Diakonikolas, Lorenzo Orecchia

This note provides a novel, simple analysis of the method of conjugate gradients for the minimization of convex quadratic functions. In contrast with standard arguments, our proof…

cs.DS2018

Fair Packing and Covering on a Relative Scale

Jelena Diakonikolas, Maryam Fazel, Lorenzo Orecchia

Fair resource allocation is a fundamental optimization problem with applications in operations research, networking, and economic and game theory. Research in these areas has led t…

math.OC2018★ 44 cited

On Acceleration with Noise-Corrupted Gradients

Michael B. Cohen, Jelena Diakonikolas, Lorenzo Orecchia

Accelerated algorithms have broad applications in large-scale optimization, due to their generality and fast convergence. However, their stability in the practical setting of noise…

math.OC2018★ 11 cited

Alternating Randomized Block Coordinate Descent

Jelena Diakonikolas, Lorenzo Orecchia

Block-coordinate descent algorithms and alternating minimization methods are fundamental optimization algorithms and an important primitive in large-scale optimization and machine…

math.OC2017★ 58 cited

The Approximate Duality Gap Technique: A Unified Theory of First-Order Methods

Jelena Diakonikolas, Lorenzo Orecchia

We present a general technique for the analysis of first-order methods. The technique relies on the construction of a duality gap for an appropriate approximation of the objective…

cs.DS2017★ 1 cited

Solving Packing and Covering LPs in Distributed Iterations with a Single Algorithm and Simpler Analysis

Jelena Diakonikolas, Lorenzo Orecchia

Packing and covering linear programs belong to the narrow class of linear programs that are efficiently solvable in parallel and distributed models of computation, yet are a powerf…

math.OC2017★ 5 cited

Accelerated Extra-Gradient Descent: A Novel Accelerated First-Order Method

Jelena Diakonikolas, Lorenzo Orecchia

We provide a novel accelerated first-order method that achieves the asymptotically optimal convergence rate for smooth functions in the first-order oracle model. To this day, Neste…