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 ...

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 ...

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

According to the latest information from the National Intellectual Property Administration, Hangzhou Dianzi University and Hangzhou Bashi Space Artificial Intelligence Information Technology Co., Ltd.

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 ...

SIAM Journal on Numerical Analysis, Vol. 51, No. 2 (2013), pp. 1134-1162 (29 pages) Alternating linear schemes (ALS), with the alternating least squares algorithm a notable special case, provide one ...

In this paper we consider ambiguous stochastic constraints under partial information consisting of means and dispersion measures of the underlying random parameters. Whereas the past literature used ...