Abstract
AbstractThis paper discusses the mathematical foundations of a technique that has been used extensively in structural optimization.1–6 Two basic problems are considered. The first of these is the concave programming problem which consists of finding the global minimum of ‘piece‐wise concave functions’ on ‘piece‐wise concave sets’. Since any function can be approximated by a piece‐wise concave function, this method could in principle be used to find the global minimum in non‐convex optimization problems. The second one is the piece‐wise linear programming problem in which the objective function is convex and piece‐wise linear. The iterative method outlined for handling this problem is shown to be much more efficient than the standard simplex method of linear programming.
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