Mathematical optimization offers today’s businesses a fundamentally different approach to worst-case scenario prepping. Rather than relying on gut instinct and static data, optimization leverages ...

Researchers have developed a new, data-driven machine-learning technique that speeds up software programs used to solve complex optimization problems that can have millions of potential solutions.

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 ...

FICO Xpress 9.8 features a GPU-accelerated implementation of the hybrid gradient algorithm, yielding up to 50x speedups. The hybrid gradient algorithm is useful for getting faster solutions to ...

By leveraging inference-time scaling and a novel "reflection" mechanism, ALE-Agent solves the context-drift problems that ...

Dr. James McCaffrey of Microsoft Research says that when quantum computing becomes generally available, evolutionary algorithms for training huge neural networks could become a very important and ...

FPMCO decomposes multi-constraint RL into KL-projection sub-problems, achieving higher reward with lower computing than second-order rivals on the new SCIG robotics benchmark.

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

There’s an old saying: When the only tool you have is a hammer, every problem looks like a nail. Sometimes referred to as “the law of the instrument,” that hammer-and-nail idea is a common pitfall in ...