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A Gradient Projection Algorithm for Relaxation Methods
March 1983 (vol. 5 no. 3)
pp. 330-332
John L. Mohammed, Artificial Intelligence Laboratory, Fair-child Central Research and Development, Palo Alto, CA 94304.
Robert A. Hummel, Courant Institute of Mathematical Sciences, New York University, New York, NY 10012.
Steven W. Zucker, Computer Vision and Robotics Laboratory, Department of Electrical Engineering, McGill University, Montreal, P.Q., Canada H3A 2A7.
We consider a particular problem which arises when apply-ing the method of gradient projection for solving constrained optimiza-tion and finite dimensional variational inequalities on the convex set formed by the convex hull of the standard basis unit vectors. The method is especially important for relaxation labeling techniques applied to problems in artificial intelligence. Zoutendijk's method for finding feasible directions, which is relatively complicated in general situations, yields a very simple finite algorithm for this problem. We present an extremely simple algorithm for performing the gradient projection and an independent verification of its correctness.
Citation:
John L. Mohammed, Robert A. Hummel, Steven W. Zucker, "A Gradient Projection Algorithm for Relaxation Methods," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 5, no. 3, pp. 330-332, March 1983, doi:10.1109/TPAMI.1983.4767394
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