Quantum walks sound abstract, but they sit at the center of a very concrete race: who will harness quantum mechanics to solve problems that overwhelm today’s most powerful supercomputers. Instead of ...

Research team debuts the first deterministic streaming algorithms for non-monotone submodular maximization, delivering superior approximation ratios with minimal memory and real-time throughput on ...

We might be witnessing the start of a new computing era where AI, cloud and quantum begin to converge in ways that redefine ...

FICO Xpress 9.8 features a GPU-accelerated implementation of the hybrid gradient algorithm, yielding up to 50x speedups for very large optimization problems FICO Xpress Optimization has the widest ...

The future of urban green space might be written in code, according to research in the International Journal of ...

Abstract: The slime mould algorithm (SMA) simulates the mechanism by which slime moulds optimize paths through chemical signaling and morphological changes, enabling efficient exploration and ...

In the field of multi-objective evolutionary optimization, prior studies have largely concentrated on the scalability of objective functions, with relatively less emphasis on the scalability of ...

The original version of this story appeared in Quanta Magazine. For computer scientists, solving problems is a bit like mountaineering. First they must choose a problem to solve—akin to identifying a ...

Abstract: In this article, the distributed form of the zeroing neural network for solving time-varying optimal problems is put forward. Compared with traditional centralized algorithms, distributed ...

It’s been difficult to find important questions that quantum computers can answer faster than classical machines, but a new algorithm appears to do it for some critical optimization tasks. For ...