Researchers from SJTU have developed a convex-optimization-based quantum process tomography method for reconstructing quantum channels, and have shown the validity to seawater channels and general ...

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

We introduce convex and, in particular semidefinite optimization methods, duality, and complexity theory to shed new light on this relation. For the single stock problem, given moments of the prices ...

IEMS 459: Convex Optimization VIEW ALL COURSE TIMES AND SESSIONS Prerequisites Linear Algebra, Calculus , Real Analysis Description The goal of this course is to investigate in-depth and to develop ...

Sparse estimation methods are aimed at using or obtaining parsimonious representations of data or models. While naturally cast as a combinatorial optimization problem, variable or feature selection ...