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A partitioned PSB method for partially separable unconstrained optimization problems
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DOI：10.1016/j.amc.2016.06.009.png)
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Abstract
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In this paper, we propose a partitioned PSB method for solving partially separable unconstrained optimization problems. By using a projection technique, we construct a sufficient descent direction. Under appropriate conditions, we show that the partitioned PSB method with projected direction is globally and superlinearly convergent for uniformly convex problems. In particular, the unit step length is accepted after finitely many iterations. Finally, some numerical results are presented, which show that the partitioned PSB method is effective and competitive. (C) 2016 Elsevier Inc. All rights reserved.
Keywords:
Partially separable optimization problems
Partitioned PSB method
Projected PSB method
Global convergence
Superlinear convergence
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