Splitting proximal with penalization schemes for additive convex hierarchical minimization problems

2018 ◽  
Vol 35 (6) ◽  
pp. 1098-1118
Author(s):  
Nimit Nimana ◽  
Narin Petrot
Keyword(s):  
Minimization Problems ◽  
Hierarchical Minimization

SOME IMPROVED ALGORITHMS ON THE SINGLE MACHINE HIERARCHICAL SCHEDULING WITH TOTAL TARDINESS AS THE PRIMARY CRITERION

2010 ◽  
Vol 27 (05) ◽  
pp. 577-585 ◽  
Author(s):  
CHENG HE ◽  
YIXUN LIN ◽  
JINJIANG YUAN
Keyword(s):  
Single Machine ◽  
Machine Scheduling ◽  
Single Machine Scheduling ◽  
Total Tardiness ◽  
Scheduling Problem ◽  
Np Hard ◽  
Assignment Problems ◽  
Running Time ◽  
Minimization Problems ◽  
Hierarchical Minimization

It is well-known that a single machine scheduling problem of minimizing the total tardiness is NP-hard. Recently, Liu, Ng and Cheng solved some special hierarchical minimization problems with total tardiness as the primary criterion by the Algorithm TAP (Two Assignment Problems) in O(n3) time. And in this paper we present some algorithms for these problems with running time O(n log n).

scholarly journals An Efficient Method for Convex Constrained Rank Minimization Problems Based on DC Programming

2016 ◽  
Vol 2016 ◽  
pp. 1-13
Author(s):  
Wanping Yang ◽  
Jinkai Zhao ◽  
Fengmin Xu
Keyword(s):  
Objective Function ◽  
Minimization Problem ◽  
Dc Programming ◽  
Rank Minimization ◽  
Closed Form Solutions ◽  
Minimization Problems ◽  
Difference Of Convex Functions ◽  
Convex Objective Function ◽  
Difference Of Convex ◽  
Cut Problems

The constrained rank minimization problem has various applications in many fields including machine learning, control, and signal processing. In this paper, we consider the convex constrained rank minimization problem. By introducing a new variable and penalizing an equality constraint to objective function, we reformulate the convex objective function with a rank constraint as a difference of convex functions based on the closed-form solutions, which can be reformulated as DC programming. A stepwise linear approximative algorithm is provided for solving the reformulated model. The performance of our method is tested by applying it to affine rank minimization problems and max-cut problems. Numerical results demonstrate that the method is effective and of high recoverability and results on max-cut show that the method is feasible, which provides better lower bounds and lower rank solutions compared with improved approximation algorithm using semidefinite programming, and they are close to the results of the latest researches.

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