Faster optimization

Optimization problems are everywhere in engineering: Balancing design tradeoffs is an optimization problem, as are scheduling and logistical planning. The theory—and sometimes the implementation—of control systems relies heavily on optimization, and so does machine learning, which has been the basis of most recent advances in artificial intelligence. At the IEEE Symposium on Foundations of Computer Science, a trio of present and past Massachusetts Institute of Technology (MIT) graduate students won a best-student-paper award for a new “cutting-plane” algorithm, a general-purpose algorithm for solving optimization problems. The algorithm improves on the running time of its most efficient predecessor, and the researchers offer some reason to think that they may have reached the theoretical limit. But they also present a new method for applying their general algorithm to specific problems, which yields…


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