The accelerating penetration of electric vehicles (EVs) is reshaping modern distribution networks by introducing substantial, time-varying charging demand and new operational uncertainties. At the same time, Vehicle-to-Grid (V2G) capable EVs offer unprecedented flexibility to support grid...

We present MUSE-BB, a branch-and-bound (B&B) based decomposition algorithm for the deterministic global solution of nonconvex two-stage stochastic programming problems. In contrast to three recent decomposition algorithms, which solve this type of problem in a projected form by nesting an inner...

Stochastic gradient descent method and its variants constitute the core optimization algorithms that achieve good convergence rates for solving machine learning problems. These rates are obtained especially when these algorithms are fine-tuned for the application at hand. Although this tuning...

In the context of high-quality development and in the face of uncertainties and risks from economic, social and political sources, we aim to understand the resilience of public-private partnership projects in megaprojects, and to improve their ability to effectively manage adversity and optimize...

In the context of high-quality development and in the face of uncertainties and risks from economic, social and political sources, we aim to understand the resilience of public-private partnership projects in megaprojects, and to improve their ability to effectively manage adversity and optimize...

In this contribution, we present a numerical analysis of the continuous stochastic gradient (CSG) method, including applications from topology optimization and convergence rates. In contrast to standard stochastic gradient optimization schemes, CSG does not discard old gradient samples from...