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For years, intermediate measurements made it hard to quantify the complexity of quantum algorithms. New work establishes that those measurements aren’t necessary after all.
Algorithms that zero in on solutions to optimization problems are the beating heart of machine reasoning. New results reveal surprising limits.
The most widely used technique for finding the largest or smallest values of a math function turns out to be a fundamentally difficult computational problem.
A recent paper set the fastest record for multiplying two matrices. But it also marks the end of the line for a method researchers have relied on for decades to make improvements.
Avi Wigderson and László Lovász won for their work developing complexity theory and graph theory, respectively, and for connecting the two fields.
By harnessing randomness, a new algorithm achieves a fundamentally novel — and faster — way of performing one of the most basic computations in math and computer science.
After 44 years, there’s finally a better way to find approximate solutions to the notoriously difficult traveling salesperson problem.
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