Based on the authors' previous work which established theoretical foundations of two, conceptual, successive convex relaxation methods, i.e., the SSDP (Successive Semide nite Programming) Relaxation Method and the SSILP (Successive Semi-In nite Linear Programming) Relaxation Method, this paper proposes their implementable variants for general quadratic optimization problems. These problems have...

Based on the authors' previous work which established theoretical foundations of two, conceptual, successive convex relaxation methods, i.e., the SSDP (Successive Semide nite Programming) Relaxation Method and the SSILP (Successive Semi-In nite Linear Programming) Relaxation Method, this paper proposes their implementable variants for general quadratic optimization problems. These problems have...

We survey a number of algorithms for the simple stochastic game problem, which is to determine the winning probability of a type of stochastic process, where the transitions are partially controlled by two players. We show that four natural approaches to solving the problem are incorrect, and present two new algorithms for the problem. The rst reduces the problem to that of nding a locally opti...

This work investigates the model update security in a collaborative learning or federated network by using covert communication. The CC uses jamming signal and multiple friendly jammers (FJs) are deployed that can offer services to owner, i.e., base station (BS). To enable BS select best FJ, lowest cost truthful auction is adopted. Then, problem formulated optimize power, transmission local acc...

We propose a novel asynchronous parallel algorithmic framework for the minimization of the sum of a smooth nonconvex function and a convex nonsmooth regularizer, subject to both convex and nonconvex constraints. The proposed framework hinges on successive convex approximation techniques and a novel probabilistic model that captures key elements of modern computational architectures and asynchro...

We present complexity and numerical results for a new asynchronous parallel algorithmic method for the minimization of the sum of a smooth nonconvex function and a convex nonsmooth regularizer, subject to both convex and nonconvex constraints. The proposed method hinges on successive convex approximation techniques and a novel probabilistic model that captures key elements of modern computation...

Models for long-term planning often lead to infinite horizon stochastic programs that offer significant challenges for computation. Finite-horizon approximations are often used in these cases but they may also become computationally difficult. In this paper, we directly solve for value functions of infinite horizon stochastic programs. We show that a successive linear approximation method conve...

Millimeter-Wave (mmWave) Massive MIMO is one of the most effective technology for fifth-generation (5G) wireless networks. It improves both spectral and energy efficiency by utilizing 30–300 GHz millimeter-wave bandwidth a large number antennas at base station. However, increasing requires radio frequency (RF) chains which results in high power consumption. In order to reduce RF chain's energy,...

We consider the fast solution of non{smooth optimization problems as resulting for example from the approximation of elliptic free boundary problems of obstacle or Stefan type. Combining well{known concepts of successive subspace correction methods with convex analysis, we derive a new class of multigrid methods which are globally convergent and have logarithmic bounds of the asymptotic converg...

Distributed computing enables large-scale computation tasks to be processed by multiple workers in parallel. However, the randomness of communication and delays across causes straggler effect, which may degrade delay performance. Coded helps mitigate but amount redundant load task assignment should carefully optimized. In this work, we consider a multi-master heterogeneous-worker distrib...