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 ...

A decomposition method for large-scale convex optimization problems with block-angular structure and many linking constraints is analysed. The method is based on a separable approximation of the ...

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 research of the systems and controls group is grounded on analytical foundations provided by various mathematical disciplines, such as control theory, optimization theory, dynamic systems and ...

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 ...

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 ...