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We introduce a new framework for the convergence analysis of a class of distributed constrained non-convex optimization algorithms in multi-agent systems. The aim is to search for local minimizers of a non-convex objective function which is supposed to be a sum of local utility functions of the agents. The algorithm under study consists of two steps: a local stochastic gradient descent at each agent...
A new memory gradient method for nonlinear equations is proposed. The method is an iterative method, it makes use of the current and previous iterations information to generate a new iteration. This makes the method more stable and more practical. The global convergence of the algorithm is on proved in the paper. Numerical experiments show the algorithm is efficient in many situations.
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