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An exact penalty method for semidefinite-box constrained low-rank matrix optimization problems
发布时间:2017-12-22     点击次数:
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报告题目: An exact penalty method for semidefinite-box constrained low-rank matrix optimization problems
报 告 人: 刘田香 博士(香港理工大学)
报告时间: 2017年12月26日 10:00--11:00
报告地点: 理学院东北楼四楼报告厅(404)
报告摘要:

In this talk, we consider a matrix optimization problem involving a semidefinitebox
constraint and a rank constraint. We penalize the rank constraint by a non-Lipschitz
function and prove that the corresponding penalty problem is exact with respect to the
original problem. Next, we present an efficient NPG algorithm to solve the penalty
problem and furthermore propose an adaptive penalty method (APM) for solving the
original problem. Finally, the efficiency of APM is shown via numerical simulations.

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