‘Breakthrough’ algorithm exponentially faster than any previous one

Eurekalert June 28, 2018
Traditionally algorithms for optimization problems narrow down the search space for the best solution one step at a time. In contrast, the new algorithm developed by researchers at Harvard University samples a variety of directions in parallel. Based on that sample, the algorithm discards low-value directions from its search space and chooses the most valuable directions to progress towards a solution. Using a data set of two million taxi trips from the New York City taxi and limousine commission, the adaptive-sampling algorithm found solutions 6 times faster. One of the biggest challenges in machine learning is finding good, representative subsets of data from large collections of images or videos to train machine learning models. This research could identify those subsets quickly and have substantial practical impact on these large-scale data summarization problems. The research will be presented at an upcoming conference… read more.

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