The aim of this study is to investigate multi-dimensional vector variational problems considering data uncertainty in each the objective functional and constraints. We establish robust necessary sufficient efficiency conditions such that any feasible solution could be weakly efficient for under consideration. Emphatically, we present scalar, vector, fractional by using notion a convex functional.

Many critical robotics applications require robustness to disturbances arising from unplanned forces, state uncertainty, and model errors. Motion planning algorithms that explicitly reason about robustness require a coupling of trajectory optimization and feedback design, where the system’s closedloop response to bounded disturbances is optimized. Due to the often-heavy computational demands of...

Robust PCA methods are typically based on batch optimization and have to load all the samples into memory during optimization. This prevents them from efficiently processing big data. In this paper, we develop an Online Robust PCA (OR-PCA) that processes one sample per time instance and hence its memory cost is independent of the number of samples, significantly enhancing the computation and st...

Spectrum zoning is the problem faced by regulators: how to allocate different bands to different rules for radio operation. This problem has largely fallen through the cracks since to the extent that they even recognize the problem’s existence, traditional communities have viewed this as “somebody else’s problem.” Engineers/information-theorists assume that economists/lawyers will handle this a...

Many critical robotics applications require robustness to disturbances arising from unplanned forces, state uncertainty, and model errors. Motion planning algorithms that explicitly reason about robustness require a coupling of trajectory optimization and feedback design, where the system’s closedloop response to bounded disturbances is optimized. Due to the often-heavy computational demands of...