An interior-point algorithm for linearly constrained convex optimization based on kernel function and application in non-negative matrix factorization

2020 ◽  
Vol 21 (3) ◽  
pp. 1019-1051
Author(s):  
S. Fathi-Hafshejani ◽  
Z. Moaberfard
Keyword(s):  
Convex Optimization ◽  
Kernel Function ◽  
Interior Point ◽  
Matrix Factorization ◽  
Interior Point Algorithm ◽  
Constrained Convex Optimization ◽  
Linearly Constrained ◽  
Linearly Constrained Convex Optimization ◽  
Non Negative Matrix Factorization

scholarly journals Improved Lagrangian-PPA based prediction correction method for linearly constrained convex optimization

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Yanfei You ◽  
Suhong Jiang
Keyword(s):  
Convex Optimization ◽  
Optimization Problem ◽  
Correction Method ◽  
Lagrangian Function ◽  
Convex Optimization Problem ◽  
Constrained Convex Optimization ◽  
Linearly Constrained ◽  
Dual Variables ◽  
Primal And Dual ◽  
Linearly Constrained Convex Optimization

<p style='text-indent:20px;'>This paper presents an improved Lagrangian-PPA based prediction correction method to solve linearly constrained convex optimization problem. At each iteration, the predictor is achieved by minimizing the proximal Lagrangian function with respect to the primal and dual variables. These optimization subproblems involved either admit analytical solutions or can be solved by a fast algorithm. The new update is generated by using the information of the current iterate and the predictor, as well as an appropriately chosen stepsize. Compared with the existing PPA based method, the parameters are relaxed. We also establish the convergence and convergence rate of the proposed method. Finally, numerical experiments are conducted to show the efficiency of our Lagrangian-PPA based prediction correction method.</p>

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