This paper presents a new method, called the "aggregate function" method, for solvingnonlinear programming problems. At first, we use the "maximum" constraint in place of theoriginal constraint set...This paper presents a new method, called the "aggregate function" method, for solvingnonlinear programming problems. At first, we use the "maximum" constraint in place of theoriginal constraint set to convert a multi-constrained optimization problem to a non-smoothbut singly constrained problem; we then employ the surrogate constraint concept and themaximum entropy principle to derive a smooth function, by which the non-smooth maximumconstraint is approximated and the original problem is converted to a smooth and singly con-strained problem; furthermore, we develop a multiplier penalty algorithm. The presentalgorithm has merits of stable and fast convergence and ease of computer implementation,and is particularly suitable to solving a nonlinear programming problem with a large num-ber of constraints.展开更多
基金Project supported by the National Natural Science Foundation of China.
文摘This paper presents a new method, called the "aggregate function" method, for solvingnonlinear programming problems. At first, we use the "maximum" constraint in place of theoriginal constraint set to convert a multi-constrained optimization problem to a non-smoothbut singly constrained problem; we then employ the surrogate constraint concept and themaximum entropy principle to derive a smooth function, by which the non-smooth maximumconstraint is approximated and the original problem is converted to a smooth and singly con-strained problem; furthermore, we develop a multiplier penalty algorithm. The presentalgorithm has merits of stable and fast convergence and ease of computer implementation,and is particularly suitable to solving a nonlinear programming problem with a large num-ber of constraints.
