Leaders run the risk of losing their strategic edge by blindly pushing AI for the sake of AI. Companies can no longer win the ...

Utilize AI to analyze application runtime data (e.g., rendering time, communication latency), obtain optimization suggestions (such as reducing component re-rendering, reusing hardware connections), ...

The leading approach to the simplex method, a widely used technique for balancing complex logistical constraints, can’t get ...

Abstract: This article studies distributed optimization problems whose goal is to minimize the sum of cost functions located among agents in a network, where communications are described by a ...

You probably don’t need more time. By Jancee Dunn When I look back on all the major decisions I’ve dithered over, I could scream. It took me a decade to commit to becoming a parent. I wavered for a ...

Search optimization now requires combining traditional SEO with AI-focused GEO and answer-driven AEO strategies AI search usage continues to grow, with 10% of US consumers currently using generative ...

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 ...

ProcessOptimizer is a Python package designed to provide easy access to advanced machine learning techniques, specifically Bayesian optimization using, e.g., Gaussian processes. Aimed at ...

If you want to accentuate the importance of a problem, it seems sensible to explain how prevalent it is. Lots of people are at risk of Alzheimer’s disease. Lots of women carry a gene that makes them ...

Abstract: This paper develops a robust neural dynamics method for the distributed time-varying optimization problem with time-varying constraints. First, instead of assuming the objective functions ...