NONLINEAR CONVEX PROGRAMMING WITH LINEAR CONSTRAINTS: INFORMATICS AND ALGORITHMS FOR A CLASS OF ADAPTIVE TWO-SEGMENT METHODS (OPERATIONS RESEARCH, CYBERNETICS, MANAGEMENT SCIENCE, ADA)

New two-segment adaptive algorithms and informatics therefor are developed for non-separable convex programming with linear inequality constraints. Convergence of the algorithms are proved and their special new parallelism properties are developed. Implementation and comparison with the Sequential Unconstrained Minimization Technique indicates superior speed for the new algorithms.