Abstarct: In this thesis, we explain about norm, Jacobian matrix, Hessian matrix, Gradient vector and convex function; next pay attention to optimality with the requirments and show concepts of stability and energy function in dynamical system. then the structure of the neural network model and mathematical model of neurons represent. we want to state short history of neural network model to solve optimization problems and previous models to solve optimization problems. The third chapter, we show one model of neural network to solve convex nonlinear optimization problems baxsed on two cases are likely, optimality, analysis of convex function, Lyapunov’s stability and Lasalle’s invariance principal and stable equilibrium point of the neural network, we proposed the optimal solution is a convex nonlinear programming problems. we also show the proposed neural network is stable in the concept of Lyapunove and globally convergent to an exact optimal solution to the original problem. several examples are given to show the effectiveness of the proposed model. The last chapter, we will describe an optimization technique that class of non-smooth optimization problems are used. To demonstrate the efficiency of the model, some examples are solved using this model.