IEMS 458: Convex Optimization VIEW ALL COURSE TIMES AND SESSIONS Prerequisites 450-2 is recommended but not required Description The course will take an in-depth look at the main concepts and ...

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 ...

The portfolio optimization model has limited impact in practice because of estimation issues when applied to real data. To address this, we adapt two machine learning methods, regularization and cross ...

Adaptive distributionally robust optimization explores strategies for decision-making under uncertainty, balancing risk and performance in diverse applications.

Various non-convex optimization algorithms are thus designed to seek an optimal solution by introducing different constraints, frameworks, and initializations.

Optimization algorithms and metaheuristics constitute a vital area of computational science, offering robust strategies for tackling complex, multidimensional problems across diverse domains.

Rice University computer scientist Anastasios Kyrillidis has won a National Science Foundation CAREER Award to explore the theory and design of non-convex optimization algorithms, an increasingly ...

MicroAlgo Inc. (the "Company" or "MicroAlgo") (NASDAQ: MLGO) announced today the launch of their latest classifier auto-optimization technology based on Variational Quantum Algorithms (VQA). This ...

Shenzhen, May. 27, 2025––MicroAlgo Inc. (the "Company" or "MicroAlgo") (NASDAQ: MLGO), today announced efforts to improve the accuracy and reliability of quantum algorithms by exploring and ...