A linearly convergent dual-based gradient projection algorithm for quadratically constrained convex minimization

Amir Beck; Marc Teboulle

doi:10.1287/moor.1060.0193

A linearly convergent dual-based gradient projection algorithm for quadratically constrained convex minimization

Amir Beck^*, Marc Teboulle

^*Corresponding author for this work

School of Mathematical Sciences

Technion-Israel Institute of Technology

Research output: Contribution to journal › Article › peer-review

8 Scopus citations

Abstract

This paper presents a new dual formulation for quadratically constrained convex programs. The special structure of the derived dual problem allows us to apply the gradient projection algorithm to produce a simple explicit method involving only elementary vector-matrix operations, proven to converge at a linear rate.

Original language	English
Pages (from-to)	398-417
Number of pages	20
Journal	Mathematics of Operations Research
Volume	31
Issue number	2
DOIs	https://doi.org/10.1287/moor.1060.0193
State	Published - 2006

Keywords

Convex minimization
Gradient projection algorithm
Quadratically constrained problems
Rate of convergence

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abstract = "This paper presents a new dual formulation for quadratically constrained convex programs. The special structure of the derived dual problem allows us to apply the gradient projection algorithm to produce a simple explicit method involving only elementary vector-matrix operations, proven to converge at a linear rate.",

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KW - Gradient projection algorithm

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KW - Rate of convergence

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A linearly convergent dual-based gradient projection algorithm for quadratically constrained convex minimization

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Keywords

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