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Sparsity in Standard Quadratic Optimization

Published on:

6 October 2023

Primary Category:

Optimization and Control

Paper Authors:

Immanuel Bomze,

Bo Peng,

Yuzhou Qiu,

E. Alper Yıldırım

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Key Details

Studies standard quadratic optimization with a hard sparsity constraint

Focuses on tractable convex relaxations from mixed-binary reformulations

Analyzes properties and relations of the relaxations

Characterizes when the relaxations are exact

Reveals limitations showing relaxations can be quite weak if sparsity is not stringent enough

AI generated summary

Sparsity in Standard Quadratic Optimization

This paper studies standard quadratic optimization problems with a hard sparsity constraint, limiting the number of nonzero variables. It focuses on tractable convex relaxations derived from mixed-binary reformulations, analyzing their properties and relations to the unrelaxed problems. Key results characterize when the relaxations are exact and reveal their limitations, showing they can be quite weak if sparsity is not stringent enough.

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