1,721,011 research outputs found
Distributed nonconvex optimization over networks
No full textWe study nonconvex distributed optimization in multi-agent networks. We introduce a novel algorithmic framework for the distributed minimization of the sum of a smooth (possibly nonconvex) function-the agents' sum-utility-plus a convex (possibly nonsmooth) regularizer. The proposed method hinges on successive convex approximation (SCA) techniques while leveraging dynamic consensus as a mechanism to distribute the computation among the agents. Asymptotic convergence to (stationary) solutions of the nonconvex problem is established. Numerical results show that the new method compares favorably to existing algorithms on both convex and nonconvex problems
Distributed nonconvex optimization over time-varying networks
No full textIn this paper we introduce a novel algorithmic framework for non-convex distributed optimization in multi-agent networks with time-varying (nonsymmetric) topology. The proposed method hinges on successive convex approximation (SCA) techniques while leveraging dynamic consensus as a mechanism to diffuse information: each agent first solves (possibly inexactly) a local convex approximation of the nonconvex original problem, and then performs local averaging operations. Asymptotic convergence to (stationary) solutions of the nonconvex problem is established. Finally, the framework is applied to a distributed nonlinear regression problem
Joint optimization of radio and computational resources for multicell mobile cloud computing
No full textWe consider a MIMO multicell system wherein several Mobile Users (MUs) ask for computation offloading to a common cloud server through their femto-access points. We formulate the computation offloading problem as a joint optimization of the radio resources the transmit precoding matrices of the MUs and the computational resources the CPU cycles/second assigned by the cloud to each MU in order to minimize the overall users' energy consumption while meeting the latency constraints imposed by the applications running on the MUs. The resulting optimization problem is nonconvex (in the objective function and the constraints), and there are constraints coupling all the optimization variables. To cope with the nonconvexity, we hinge on successive convex approximation techniques and propose an iterative algorithm converging to a local optimal solution of the original nonconvex problem. The algorithm is also suitable for a parallel implementation across the access point, with limited coordination/signaling with the cloud. Numerical results show that the proposed joint optimization yields significant energy savings with respect to more traditional schemes performing a separate optimization of the radio and computational resources
Distributed mobile cloud computing. joint optimization of radio and computational resources
No full textWe consider a scenario composed by multiple mobile users asking for computation offloading of their applications to a set of cloud servers. A set of radio access points, small cell base stations possibly coexisting with macro base stations, are available to provide radio proximity access to fixed computational resources. Our objective is to find the optimal assignment of each mobile user to a cloud server through the most convenient base station and, contextually, the optimal MIMO precoding matrices and computational rates (virtual machines) to each user, under latency constraints dictated by the users Quality of Experience (QoE). The radio resources assigned to users belonging to the same cell are orthogonal to each other, whereas users of different cells might interfere against each other. The latency constraint imposes a strict relationship between the time spent for transferring the program execution from the mobile device to the fixed server (and viceversa) and the time needed to execute the computation. To properly exploit this relationship, we formulate the computation offloading problem as a joint optimization of the radio and computational resources, with the objective of minimizing the overall energy consumption, at the mobile terminal side, while meeting the latency constraints. The resulting optimization problem is nonconvex in both the objective function and in the constraints. Nevertheless, by hinging on successive convex approximation techniques, we propose an iterative algorithm able to converge to a local optimal solution of the original nonconvex problem
Ghost penalties in nonconvex constrained optimization: Diminishing stepsizes and iteration complexity
No full textWe consider nonconvex constrained optimization problems and propose a new approach to the convergence analysis based on penalty functions. We make use of classical penalty functions in an unconventional way, in that penalty functions only enter in the theoretical analysis of convergence while the algorithm itself is penalty free. Based on this idea, we are able to establish several new results, including the first general analysis for diminishing stepsize methods in nonconvex, constrained optimization, showing convergence to generalized stationary points, and a complexity study for sequential quadratic programming–type algorithms
Diminishing stepsize methods for nonconvex composite problems via ghost penalties: from the general to the convex regular constrained case
Full text linkIn this paper, we first extend the diminishing stepsize method for nonconvex constrained problems presented in F. Facchinei, V. Kungurtsev, L. Lampariello and G. Scutari [Ghost penalties in nonconvex constrained optimization: Diminishing stepsizes and iteration complexity, To appear on Math. Oper. Res. 2020. Available at https://arxiv.org/abs/1709.03384.] to deal with equality constraints and a nonsmooth objective function of composite type. We then consider the particular case in which the constraints are convex and satisfy a standard constraint qualification and show that in this setting the algorithm can be considerably simplified, reducing the computational burden of each iteration
Effect of 2,6-diisopropylphenol and halogenated anesthetics on tetraphenylphosphonium uptake by rat brain synaptosomes. Determination of membrane potential
No full textDistributed joint optimization of radio and computational resources for mobile cloud computing
No full textMobile computation offloading of computational intensive tasks from mobile devices to surrogate cloud servers has been recently envisaged as a promising technique to enhance the computational capacity of the mobile devices. Within this framework we consider a MIMO multicell system wherein several Mobile Users (MUs) ask for computation offloading to a common cloud server through their femto-access points. We formulate the computation offloading problem as a joint optimization of the radio and computational resources in order to minimize the overall users' energy consumption while meeting the latency constraints imposed by the applications. To solve this non-convex problem we hinge on successive convex approximation techniques by showing that the original problem can be decomposed in parallel convex subproblems. Hence we devise an iterative algorithm which can be implemented in a distributed manner across the access points through dual/primal decomposition techniques requiring limited coordination/signaling with the cloud
Multi-Agent asynchronous nonconvex large-scale optimization
No full textWe propose a novel algorithmic framework for the asynchronous and distributed optimization of multi-agent systems. We consider the constrained minimization of a nonconvex and nonsmooth partially separable sum-utility function, i.e., the cost function of each agent depends on the optimization variables of that agent and of its neighbors. This partitioned setting arises in several applications of practical interest. The proposed algorithmic framework is distributed and asynchronous: i) agents update their variables at arbitrary times, without any coordination with the others; and ii) agents may use outdated information from their neighbors. Convergence to stationary solutions is proved, and theoretical complexity results are provided, showing nearly ideal linear speedup with respect to the number of agents, when the delays are not too large
- …
