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    STUDIA INFORMATICA - Issue no. 1 / 2009  
         
  Article:   A METHOD WITH RANDOM MODIFICATION OF GRADIENT COMPONENTS FOR CONVEX MODELS.

Authors:  PAVEL BĂLAN. 		 
       
         
  Abstract:  A stochastic method is proposed and analyzed, that is a probabilistic generalization of gradient method, for solving convex models with restrictions. A random change of "old" partial derivatives with "new" ones is performed from one iteration to another. Convergence aspects of this method are analyzed for the case when the step is adjusted programmatically. Certain conditions are indicated, that ensure its convergence to the optimal solution with probability 1.

Key words and phrases. stochastic method, differentiable optimization, probabilistic generalization of gradient method, convex model, partial derivatives, movement direction, convergence, with probability 1, almost sure, connect-disconnect type.

  
         
     
         
         
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