In this paper,the problem of computation offloading in the edge server is studied in a mobile edge computation(MEC)-enabled cell networks that consists of a base station(BS)integrating edge servers,several terminal de...In this paper,the problem of computation offloading in the edge server is studied in a mobile edge computation(MEC)-enabled cell networks that consists of a base station(BS)integrating edge servers,several terminal devices and collaborators.In the considered networks,we develop an intelligent task offloading and collaborative computation scheme to achieve the optimal computation offloading.First,a distance-based collaborator screening method is proposed to get collaborators within the distance threshold and with high power.Second,based on the Lyapunov stochastic optimization theory,the system stability problem is transformed into a queue stability issue,and the optimal computation offloading is obtained by solving these three sub-problems:task allocation control,task execution control and queue update,respectively.Moreover,rigorous experimental simulation shows that our proposed computation offloading algorithm can achieve the joint optimization among the system efficiency,energy consumption and time delay compared to the mobility-aware and migration-enabled approach,Full BS and Full local.展开更多
The fuzzy sets theory is introduced into the fatigue reliability analysis. The concepts of maximizing set and minimizing set are developed to decide the ordering value of each fuzzy number, and these values can be use...The fuzzy sets theory is introduced into the fatigue reliability analysis. The concepts of maximizing set and minimizing set are developed to decide the ordering value of each fuzzy number, and these values can be used to determine the order of the fuzzy numbers. On the basis of the works mentioned above, the membership function defining the fuzzy safety event can be calculated, and then the fuzzy reliability in the case of stress and fuzzy fatigue strength is deduced. An example is given to illustrate the method.展开更多
We propose a novel dynamic asset allocation framework based on a family of mean-variance-induced utility functions that alleviate the non-monotonicity and time-inconsistency problems of mean-variance optimization.The ...We propose a novel dynamic asset allocation framework based on a family of mean-variance-induced utility functions that alleviate the non-monotonicity and time-inconsistency problems of mean-variance optimization.The utility functions are motivated by the equivalence between the mean-variance objective and a quadratic utility function.Crucially,our framework differs from mean-variance analysis in that we allow different treatment of upside and downside deviations from a target wealth level.This naturally leads to a different characterization of possible investment outcomes below and above a target wealth as risk and potential.Our proposed asset allocation framework retains two attractive features of mean-variance optimization:an intuitive explanation of the investment objective and an easily computed optimal strategy.We establish a semi-analytical solution for the optimal trading strategy in our framework and provide numerical examples to illustrate its behavior.Finally,we discuss applications of this framework to robo-advisors.展开更多
The author studies the optimal investment stopping problem in both continuous and discrete cases, where the investor needs to choose the optimal trading strategy and optimal stopping time concurrently to maximize the ...The author studies the optimal investment stopping problem in both continuous and discrete cases, where the investor needs to choose the optimal trading strategy and optimal stopping time concurrently to maximize the expected utility of terminal wealth.Based on the work of Hu et al.(2018) with an additional stochastic payoff function,the author characterizes the value function for the continuous problem via the theory of quadratic reflected backward stochastic differential equations(BSDEs for short) with unbounded terminal condition. In regard to the discrete problem, she gets the discretization form composed of piecewise quadratic BSDEs recursively under Markovian framework and the assumption of bounded obstacle, and provides some useful a priori estimates about the solutions with the help of an auxiliary forward-backward SDE system and Malliavin calculus. Finally, she obtains the uniform convergence and relevant rate from discretely to continuously quadratic reflected BSDE, which arise from corresponding optimal investment stopping problem through above characterization.展开更多
We study an optimal investment problem under default risk where related information such as loss or recovery at default is considered as an exogenous ran-dom mark added at default time.Two types of agents who have dif...We study an optimal investment problem under default risk where related information such as loss or recovery at default is considered as an exogenous ran-dom mark added at default time.Two types of agents who have different levels of information are considered.We first make precise the insider’s information flow by using the theory of enlargement of filtrations and then obtain explicit logarith-mic utility maximization results to compare optimal wealth for the insider and the ordinary agent.展开更多
In this paper,we study the joint bandwidth allocation and path selection problem,which is an extension of the well-known network utility maximization(NUM)problem,via solving a multi-objective minimization problem unde...In this paper,we study the joint bandwidth allocation and path selection problem,which is an extension of the well-known network utility maximization(NUM)problem,via solving a multi-objective minimization problem under path cardinality constraints.Specifically,such a problem formulation captures various types of objectives including proportional fairness,average delay,as well as load balancing.In addition,in order to handle the"unsplittable flows",path cardinality constraints are added,making the resulting optimization problem quite challenging to solve due to intrinsic nonsmoothness and nonconvexity.Almost all existing works deal with such a problem using relaxation techniques to transform it into a convex optimization problem.However,we provide a novel solution framework based on the linearized alternating direction method of multipliers(LADMM)to split the original problem with coupling terms into several subproblems.We then derive that these subproblems,albeit nonconvex nonsmooth,are actually simple to solve and easy to implement,which can be of independent interest.Under some mild assumptions,we prove that any limiting point of the generated sequence of the proposed algorithm is a stationary point.Numerical simulations are performed to demonstrate the advantages of our proposed algorithm compared with various baselines.展开更多
基金supported by Qinghai Natural Science Foundation under No.2020-ZJ-943Q.
文摘In this paper,the problem of computation offloading in the edge server is studied in a mobile edge computation(MEC)-enabled cell networks that consists of a base station(BS)integrating edge servers,several terminal devices and collaborators.In the considered networks,we develop an intelligent task offloading and collaborative computation scheme to achieve the optimal computation offloading.First,a distance-based collaborator screening method is proposed to get collaborators within the distance threshold and with high power.Second,based on the Lyapunov stochastic optimization theory,the system stability problem is transformed into a queue stability issue,and the optimal computation offloading is obtained by solving these three sub-problems:task allocation control,task execution control and queue update,respectively.Moreover,rigorous experimental simulation shows that our proposed computation offloading algorithm can achieve the joint optimization among the system efficiency,energy consumption and time delay compared to the mobility-aware and migration-enabled approach,Full BS and Full local.
基金This project is supported by National Naied Science Foundation of China(59475043). Manuscript received on July 8,1999 revised m
文摘The fuzzy sets theory is introduced into the fatigue reliability analysis. The concepts of maximizing set and minimizing set are developed to decide the ordering value of each fuzzy number, and these values can be used to determine the order of the fuzzy numbers. On the basis of the works mentioned above, the membership function defining the fuzzy safety event can be calculated, and then the fuzzy reliability in the case of stress and fuzzy fatigue strength is deduced. An example is given to illustrate the method.
基金supported by the National Natural Science Foundation of China(Nos.71671106 and 72171138)by the Shanghai Institute of International Finance and Economics,and by the Program for Innovative Research Team of Shanghai University of Finance and Economics(No.2020110930)+1 种基金partially supported by the Research Grants Council of the Hong Kong Special Administrative Region,China(No.CityU 11200219)partially supported by the National Natural Science Foundation of China(No.72050410356).
文摘We propose a novel dynamic asset allocation framework based on a family of mean-variance-induced utility functions that alleviate the non-monotonicity and time-inconsistency problems of mean-variance optimization.The utility functions are motivated by the equivalence between the mean-variance objective and a quadratic utility function.Crucially,our framework differs from mean-variance analysis in that we allow different treatment of upside and downside deviations from a target wealth level.This naturally leads to a different characterization of possible investment outcomes below and above a target wealth as risk and potential.Our proposed asset allocation framework retains two attractive features of mean-variance optimization:an intuitive explanation of the investment objective and an easily computed optimal strategy.We establish a semi-analytical solution for the optimal trading strategy in our framework and provide numerical examples to illustrate its behavior.Finally,we discuss applications of this framework to robo-advisors.
基金This work was supported by the China Scholarship Councilthe National Science Foundation of China(No.11631004)the Science and Technology Commission of Shanghai Municipality(No.14XD1400400)。
文摘The author studies the optimal investment stopping problem in both continuous and discrete cases, where the investor needs to choose the optimal trading strategy and optimal stopping time concurrently to maximize the expected utility of terminal wealth.Based on the work of Hu et al.(2018) with an additional stochastic payoff function,the author characterizes the value function for the continuous problem via the theory of quadratic reflected backward stochastic differential equations(BSDEs for short) with unbounded terminal condition. In regard to the discrete problem, she gets the discretization form composed of piecewise quadratic BSDEs recursively under Markovian framework and the assumption of bounded obstacle, and provides some useful a priori estimates about the solutions with the help of an auxiliary forward-backward SDE system and Malliavin calculus. Finally, she obtains the uniform convergence and relevant rate from discretely to continuously quadratic reflected BSDE, which arise from corresponding optimal investment stopping problem through above characterization.
文摘We study an optimal investment problem under default risk where related information such as loss or recovery at default is considered as an exogenous ran-dom mark added at default time.Two types of agents who have different levels of information are considered.We first make precise the insider’s information flow by using the theory of enlargement of filtrations and then obtain explicit logarith-mic utility maximization results to compare optimal wealth for the insider and the ordinary agent.
基金supported by the National Natural Science Foundation of China under Grant 11831002。
文摘In this paper,we study the joint bandwidth allocation and path selection problem,which is an extension of the well-known network utility maximization(NUM)problem,via solving a multi-objective minimization problem under path cardinality constraints.Specifically,such a problem formulation captures various types of objectives including proportional fairness,average delay,as well as load balancing.In addition,in order to handle the"unsplittable flows",path cardinality constraints are added,making the resulting optimization problem quite challenging to solve due to intrinsic nonsmoothness and nonconvexity.Almost all existing works deal with such a problem using relaxation techniques to transform it into a convex optimization problem.However,we provide a novel solution framework based on the linearized alternating direction method of multipliers(LADMM)to split the original problem with coupling terms into several subproblems.We then derive that these subproblems,albeit nonconvex nonsmooth,are actually simple to solve and easy to implement,which can be of independent interest.Under some mild assumptions,we prove that any limiting point of the generated sequence of the proposed algorithm is a stationary point.Numerical simulations are performed to demonstrate the advantages of our proposed algorithm compared with various baselines.