Mathematical researchers have, voer the past decade, developed an efficient class of linear programming solvers known as interior-point methods. Interior-point methods have theoretical and observed computational advantage over simplex methods at solving many large linear programming problems and are immune to degeneracy. Common nonlinear programming methods which work well for small and medium sized problems are unable to solve loarge-scale problems in a timely fashion. Used in an adaptive sequential linear programming strategy, interior-point methods can be a poserful engineering optimization tool. This work demonstrates the application of an adaptive sequential linear programming alogorithm that uses an infeasible primaldual path-following interior-point algorithm and fuzzy heuristics for the solution of large-scale engineering design optimization problems. Numerical examples demonstrate the superiority of interior-piont methods compared to well-known simplex-based linear solver in solving large optimum design problems. Superior per-formance is shown in both computational time and algorithm ability to handle degenerate problems.
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