An Algorithm for Separable Nonconvex Programming Problems II: Nonconvex Constraints

We extend a previous algorithm in order to solve mathematical programming problems of the form: Find x = (x 1 , …, x n ) to minimize ∑φ i0 (x i ) subject to x ∈ G, l ≦ x ≦ L and ∑φ ij (x i ) ≦ 0, j = 1, …, m. Each φ ij is assumed to be lower semicontinuous, possibly nonconvex, and G is assumed to be closed. The algorithm is of the branch and bound type and solves a sequence of problems in each of which the objective function is convex. In case G is convex each problem in the sequence is a convex programming problem. The problems correspond to successive partitions of the set C = { x ∣ l ≦ x ≦ L}. Two different rules for refining the partitions are considered; these lead to convergence of the algorithm under different requirements on the problem functions. An example is given, and computational considerations are discussed.

• 저널Management Science · 17(11) · 759–773
• 토픽Advanced Optimization Algorithms Research · Numerical Analysis
• DOI10.1287/mnsc.17.11.759
• 저자Richard M. Soland