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A New Super-memory Gradient Method with Curve Search Rule for Unconstrained Minimization Problems

Jingyong Tang, Li Dong, Jinchuan Zhou


In this paper we present a new super-memory gradient method for unconstrained minimization problems. The global convergence and linear convergence rate of this method are proved under some mild conditions. The method uses previous multi-step iterative information and a curve search rule to generate new iterative points. It is particular that the search direction and the step-size are determined simultaneously at each iteration. This
makes the new method converge more stably than other similar methods and be suitable for solving large scale unconstrained minimization problems. Numerical results show that our method is effective in practical computation.


unconstrained minimization problem, super-memory gradient method, curve search, global convergence, linear convergence rate.

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