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2016

A Lagrangian-DNN relaxation: a fast method for computing tight lower bounds for a class of quadratic optimization problems

4 years 3 months ago
A Lagrangian-DNN relaxation: a fast method for computing tight lower bounds for a class of quadratic optimization problems
We propose an efficient computational method for linearly constrained quadratic optimization problems (QOPs) with complementarity constraints based on their Lagrangian and doubly nonnegative (DNN) relaxation and first-order algorithms. The simplified Lagrangian-CPP relaxation of such QOPs proposed by Arima, Kim, and Kojima in 2012 takes one of the simplest forms, an unconstrained conic linear optimization problem with a single Lagrangian parameter in a completely positive (CPP) matrix variable with its
Sunyoung Kim, Masakazu Kojima, Kim-Chuan Toh
Added 08 Apr 2016
Updated 08 Apr 2016
Type Journal
Year 2016
Where MP
Authors Sunyoung Kim, Masakazu Kojima, Kim-Chuan Toh
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