This paper proposes a new method to solve general constrained optimization problem. The problem of finding the local optimal points of a nonlinear programming problem with equality and inequality constraints is considered by solving the ODE (i.e., ordinary differential equation) with an appropriate numerical procedure. Moreover, the rate of convergence to optimal points is quadratic. Some numerical result is given to show the efficiency of the proposed method.

This paper proposes a convergence theory of the ordinary differential equations ODE. method for finding the local optima of general constrained optimization. We prove that solutions starting from the neighbourhood of a critical point of the differential equations in this paper about part variables always converge to the feasible point of the problem. We also study the initial relationship between neural computation in optimization NCO. and ODE methods. Some detailed connections of the two methods in solving the linear and quadratic programming on Rqn are shown in this paper.

Combined forecasting is a well-known forecasting technique of handling multiple forecasting methods. However, the previous combined forecasting approaches lack of inco orating the various possible opinions from several experts on an given forecasting problem. This paper proposes an MC2 linear programming approach to determining weighted coefficients of combined forecasting that involves multiple experts. The numerical example of the paper shows that the proposed appreach likely outperforms the current techniques of combined forecasting in dealing with the case of multiple experts.

任何投资者都希望在投资中获取大的回报，但较大的回报通常伴随着较大的风险。为了分散风险或减少风险，投资组合是一种常用的投资技术。本文提出一种新的投资组合方法，可以同时概括投资项目的多种预测结果和多位投资专家的观点，在综合多方面投资建议的基础上，制定投资方案。

In this paper, we describe a mathematical framework of estimating the er-ror bounds of optimal combined forecasting (OCF) and the super-bound of nonoptimal combined forecasting errors. We also determine the existent interval [x,i,/n, X,,, /n] of the minimal errors square sum J of optimal combined forecasting. Furthermore, we give the relationships between the errors of the general combined forecasting method (including the simple average method) and the errors of each forecasting model in the combination by mathematics analysis.