The goal of this course is to investigate in-depth and to develop expert knowledge in the theory and algorithms for convex optimization. This course will provide a rigorous introduction to the rich ...

Abstract. We consider sensitivity functionals and Lagrange multiplier method for solving finite dimensional convex optimization problem.An analysis based on this property is also applied for ...

This course discusses basic convex analysis (convex sets, functions, and optimization problems), optimization theory (linear, quadratic, semidefinite, and geometric programming; optimality conditions ...

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 ...

This is a preview. Log in through your library . Abstract We apply conjugate duality to establish the existence of optimal portfolios in an assetallocation problem, with the goal of minimizing the ...

Quantum process tomography is often used to completely characterize an unknown quantum process. However, it may lead to an unphysical process matrix, which will cause the loss of information with ...

What are some recent advances in non-convex optimization research? originally appeared on Quora - the knowledge sharing network where compelling questions are answered by people with unique insights.

Entransy theory has emerged as a powerful framework for optimising heat transfer processes by quantifying a system’s capacity to transfer thermal energy in a manner analogous to electrical energy ...

RIKEN Center for Advanced Intelligence Project (AIP) houses more than 40 research teams ranging from fundamentals of machine learning and optimization, applications in medicine, materials, and ...