Multifidelity optimization can inform decision-making during process development and reduce the number of experiments ...

This course offers an introduction to mathematical nonlinear optimization with applications in data science. The theoretical foundation and the fundamental algorithms for nonlinear optimization are ...

Optimization seeks to find the best. It could be to design a process that minimizes capital or maximizes material conversion, to choose operating conditions that maximize throughput or minimize waste, ...

where \(\mathsf{G}(\cdot)\) is some convex operator and \(\mathcal{F}\) is as set of feasible input distributions. Examples of such an optimization problem include finding capacity in information ...

Rajalakshmi Srinivasaraghavan highlights hybrid AI compiler and runtime optimizations balancing CPUs, GPUs, and accelerators.

Boost website speed optimization in 2026 with techniques like caching, image compression, and CDNs for faster page load improvement and better user retention.