Equivalent stochastic linearization （ESL） for nonlinear uncertain structure under stationary stochastic excitation is presented. There are two parts of difference between the original system and equivalent system： ...Equivalent stochastic linearization （ESL） for nonlinear uncertain structure under stationary stochastic excitation is presented. There are two parts of difference between the original system and equivalent system： one is caused by the difference between the means of original and equivalent stochastic structure; and another is caused by the difference between the original and equivalent stochastic structure which has the relation with stochastic variables. Statistical characteristics of equivalent stochastic structure can be obtained in accordance with mean square criterion, so nonlinear stochastic structure is transformed into linear stochastic structure. In order to attain that objective, the compound response spectrum of linear stochastic structure under stationary random excitation which is used in the solution is derived in the case of the mutual independence between stochastic excitation and stochastic structure. Finally, the example shows the accuracy and validity of the proposed method.展开更多
This paper investigates the robust stochastic stability and H∞ analysis for stochastic systems with time-varying delay and Markovian jump. By using the freeweighting matrix technique, i.e., He＇s technique, and a sto...This paper investigates the robust stochastic stability and H∞ analysis for stochastic systems with time-varying delay and Markovian jump. By using the freeweighting matrix technique, i.e., He＇s technique, and a stochastic Lyapunov-Krasovskii functional, new delay-dependent criteria in terms of linear matrix inequalities are derived for the the robust stochastic stability and the H∞ disturbance attenuation. Three numerical examples axe given. The results show that the proposed method is efficient and much less conservative than the existing results in the literature.展开更多
Utilizing the well-known aggregation technique, we propose a smoothing sample average approximation （SAA） method for a stochastic linear complementarity problem, where the underlying functions are represented by exp...Utilizing the well-known aggregation technique, we propose a smoothing sample average approximation （SAA） method for a stochastic linear complementarity problem, where the underlying functions are represented by expectations of stochastic functions. The method is proved to be convergent and the preliminary numerical results are reported.展开更多
In this paper, a class of the stochastic generalized linear complementarity problems with finitely many elements is proposed for the first time. Based on the Fischer-Burmeister function, a new conjugate gradient proje...In this paper, a class of the stochastic generalized linear complementarity problems with finitely many elements is proposed for the first time. Based on the Fischer-Burmeister function, a new conjugate gradient projection method is given for solving the stochastic generalized linear complementarity problems. The global convergence of the conjugate gradient projection method is proved and the related numerical results are also reported.展开更多
In order to simulate a linear stochastic oscillator with additive noise,improved nonstandard optimal(INSOPT) schemes are derived utilizing the nonstandard finite difference(NSFD)technique and the improvement technique...In order to simulate a linear stochastic oscillator with additive noise,improved nonstandard optimal(INSOPT) schemes are derived utilizing the nonstandard finite difference(NSFD)technique and the improvement technique.These proposed schemes reproduce long time features of the oscillator solution exactly.Their abilities in preserving the symplecticity,the linear growth property of the second moment and the oscillation property of the solution of the stochastic oscillator system on long time interval are studied.It can be shown that the component { x_n}_(n≥1) of the INSOPT schemes switch signs infinitely many times as n →∞,almost surely.Further,the mean-square convergence order of 1 is obtained for these INSOPT schemes.Finally,numerical experiments illustrate intuitively the results obtained in this paper.展开更多
A technique is developed for finding a closed form expression for the cumulative distribution function of the maximum value of the objective function in a stochastic linear programming problem, where either the object...A technique is developed for finding a closed form expression for the cumulative distribution function of the maximum value of the objective function in a stochastic linear programming problem, where either the objective function coefficients or the right hand side coefficients are continuous random vectors with known probability distributions. This is the “wait and see” problem of stochastic linear programming. Explicit results for the distribution problem are extremely difficult to obtain;indeed, previous results are known only if the right hand side coefficients have an exponential distribution [1]. To date, no explicit results have been obtained for stochastic c, and no new results of any form have appeared since the 1970’s. In this paper, we obtain the first results for stochastic c, and new explicit results if b an c are stochastic vectors with an exponential, gamma, uniform, or triangle distribution. A transformation is utilized that greatly reduces computational time.展开更多
This paper investigates the random responses of a TDOF structure with strongly nonlinear coupling and parametric vibration. With the nonlinear cou- pling of inertia in the equations of motion of the system being remov...This paper investigates the random responses of a TDOF structure with strongly nonlinear coupling and parametric vibration. With the nonlinear cou- pling of inertia in the equations of motion of the system being removed by successive elimination, the non-Gaussian moment equation method (NGM) is applied and 69 moment equations are integrated with central cumulative truncation technique. The stochastic central difference-cum-statistical linearization method(SCD-SL) and the digital simulation method(DSM) are also used. A comparison of results by different methods are given and the SCD-SL method is the most efficient method.展开更多
Modern financial theory, commonly known as portfolio theory, provides an analytical framework for the investment decision to be made under uncertainty. It is a well-established proposition in portfolio theory that whe...Modern financial theory, commonly known as portfolio theory, provides an analytical framework for the investment decision to be made under uncertainty. It is a well-established proposition in portfolio theory that whenever there is an imperfect correlation between returns risk is reduced by maintaining only a portion of wealth in any asset, or by selecting a portfolio according to expected returns and correlations between returns. The major improvement of the portfolio approaches over prior received theory is the incorporation of 1) the riskiness of an asset and 2) the addition from investing in any asset. The theme of this paper is to discuss how to propose a new mathematical model like that provided by Markowitz, which helps in choosing a nearly perfect portfolio and an efficient input/output. Besides applying this model to reality, the researcher uses game theory, stochastic and linear programming to provide the model proposed and then uses this model to select a perfect portfolio in the Cairo Stock Exchange. The results are fruitful and the researcher considers this model a new contribution to previous models.展开更多
In this paper,we focus on a control-constrained stochastic LQ optimal control problem via backward stochastic differential equation(BSDE in short)with deterministic coefficients.One of the significant features in this...In this paper,we focus on a control-constrained stochastic LQ optimal control problem via backward stochastic differential equation(BSDE in short)with deterministic coefficients.One of the significant features in this framework,in contrast to the classical LQ issue,embodies that the admissible control set needs to satisfy more than the square integrability.By introducing two kinds of new generalized Riccati equations,we are able to announce the explicit optimal control and the solution to the corresponding H-J-B equation.A linear quadratic recursive utility portfolio optimization problem in the financial engineering is discussed as an explicitly illustrated example of the main result with short-selling prohibited.Feasibility of the mean-variance portfolio selection problem via BSDE for a financial market is characterized,and associated efficient portfolios are given in a closed form.展开更多
The author studies a stochastic linear quadratic(SLQ for short)optimal control problem for systems governed by stochastic evolution equations,where the control operator in the drift term may be unbounded.Under the con...The author studies a stochastic linear quadratic(SLQ for short)optimal control problem for systems governed by stochastic evolution equations,where the control operator in the drift term may be unbounded.Under the condition that the cost functional is uniformly convex,the well-posedness of the operator-valued Riccati equation is proved.Based on that,the optimal feedback control of the control problem is given.展开更多
In this paper, tile authors first study two kinds of stochastic differential equations （SDEs） with Levy processes as noise source. Based on the existence and uniqueness of the solutions of these SDEs and multi-dimen...In this paper, tile authors first study two kinds of stochastic differential equations （SDEs） with Levy processes as noise source. Based on the existence and uniqueness of the solutions of these SDEs and multi-dimensional backward stochastic differential equations （BSDEs） driven by Levy pro- cesses, the authors proceed to study a stochastic linear quadratic （LQ） optimal control problem with a Levy process, where the cost weighting matrices of the state and control are allowed to be indefinite. One kind of new stochastic Riccati equation that involves equality and inequality constraints is derived from the idea of square completion and its solvability is proved to be sufficient for the well-posedness and the existence of optimal control which can be of either state feedback or open-loop form of the LQ problems. Moreover, the authors obtain the existence and uniqueness of the solution to the Riccati equation for some special cases. Finally, two examples are presented to illustrate these theoretical results.展开更多
One of the fundamental issues in Control Theory is to design feedback controls.It is well-known that,the purpose of introducing Riccati equations in the study of deterministic linear quadratic control problems is exac...One of the fundamental issues in Control Theory is to design feedback controls.It is well-known that,the purpose of introducing Riccati equations in the study of deterministic linear quadratic control problems is exactly to construct the desired feedbacks.To date,the same problem in the stochastic setting is only partially well-understood.In this paper,we establish the equivalence between the existence of optimal feedback controls for the stochastic linear quadratic control problems with random coefficients and the solvability of the corresponding backward stochastic Riccati equations in a suitable sense.We also give a counterexample showing the nonexistence of feedback controls to a solvable stochastic linear quadratic control problem.This is a new phenomenon in the stochastic setting,significantly different from its deterministic counterpart.展开更多
In this work,the author proposes a discretization for stochastic linear quadratic control problems(SLQ problems)subject to stochastic differential equations.The author firstly makes temporal discretization and obtains...In this work,the author proposes a discretization for stochastic linear quadratic control problems(SLQ problems)subject to stochastic differential equations.The author firstly makes temporal discretization and obtains SLQ problems governed by stochastic difference equations.Then the author derives the convergence rates for this discretization relying on stochastic differential/difference Riccati equations.Finally an algorithm is presented.Compared with the existing results relying on stochastic Pontryagin-type maximum principle,the proposed scheme avoids solving backward stochastic differential equations and/or conditional expectations.展开更多
In this paper,a stochastic linear two-step scheme has been presented to approximate backward stochastic differential equations(BSDEs).A necessary and sufficient condition is given to judge the L 2-stability of our num...In this paper,a stochastic linear two-step scheme has been presented to approximate backward stochastic differential equations(BSDEs).A necessary and sufficient condition is given to judge the L 2-stability of our numerical schemes.This stochastic linear two-step method possesses a family of 3-order convergence schemes in the sense of strong stability.The coefficients in the numerical methods are inferred based on the constraints of strong stability and n-order accuracy(n∈N^(+)).Numerical experiments illustrate that the scheme is an efficient probabilistic numerical method.展开更多
Using theory of Bayesian Dynamic Models and Forecasting , this paper mainly deals with the problem on state estimation for singular discrete time stochastic linear system. And a new method of state estimation l...Using theory of Bayesian Dynamic Models and Forecasting , this paper mainly deals with the problem on state estimation for singular discrete time stochastic linear system. And a new method of state estimation linear Bayes estimation (LBE for short) has been proposed.展开更多
In this paper,the authors consider a sparse parameter estimation problem in continuoustime linear stochastic regression models using sampling data.Based on the compressed sensing(CS)method,the authors propose a compre...In this paper,the authors consider a sparse parameter estimation problem in continuoustime linear stochastic regression models using sampling data.Based on the compressed sensing(CS)method,the authors propose a compressed least squares(LS) algorithm to deal with the challenges of parameter sparsity.At each sampling time instant,the proposed compressed LS algorithm first compresses the original high-dimensional regressor using a sensing matrix and obtains a low-dimensional LS estimate for the compressed unknown parameter.Then,the original high-dimensional sparse unknown parameter is recovered by a reconstruction method.By introducing a compressed excitation assumption and employing stochastic Lyapunov function and martingale estimate methods,the authors establish the performance analysis of the compressed LS algorithm under the condition on the sampling time interval without using independence or stationarity conditions on the system signals.At last,a simulation example is provided to verify the theoretical results by comparing the standard and the compressed LS algorithms for estimating a high-dimensional sparse unknown parameter.展开更多
The authors give a stochastic maximum principle for square-integrable optimal control of linear stochastic systems.The control domain is not necessarily convex and the cost functional can have a quadratic growth.In pa...The authors give a stochastic maximum principle for square-integrable optimal control of linear stochastic systems.The control domain is not necessarily convex and the cost functional can have a quadratic growth.In particular,they give a stochastic maximum principle for the linear quadratic optimal control problem.展开更多
Based on the theory of Bayes forecasting, this paper mainly deals with the problem onthe state estimation for singular discrete-time stochastic linear system. And a new approach to optimalfiltering-linear Bayes estima...Based on the theory of Bayes forecasting, this paper mainly deals with the problem onthe state estimation for singular discrete-time stochastic linear system. And a new approach to optimalfiltering-linear Bayes estimation (LBE) has been proposed.展开更多
This paper reviews the mean field social(MFS)optimal control problem for multi-agent dynamic systems and the mean-field-type(MFT)optimal control problem for single-agent dynamic systems within the linear quadratic(LQ)...This paper reviews the mean field social(MFS)optimal control problem for multi-agent dynamic systems and the mean-field-type(MFT)optimal control problem for single-agent dynamic systems within the linear quadratic(LQ)framework.For the MFS control problem,this review discusses the existing conclusions on optimization in dynamic systems affected by both additive and multiplicative noises.In exploring MFT optimization,the authors first revisit researches associated with single-player systems constrained by these dynamics.The authors then extend the proposed review to scenarios that include multiple players engaged in Nash games,Stackelberg games,and cooperative Pareto games.Finally,the paper concludes by emphasizing future research on intelligent algorithms for mean field optimization,particularly using reinforcement learning method to design strategies for models with unknown parameters.展开更多
文摘Equivalent stochastic linearization （ESL） for nonlinear uncertain structure under stationary stochastic excitation is presented. There are two parts of difference between the original system and equivalent system： one is caused by the difference between the means of original and equivalent stochastic structure; and another is caused by the difference between the original and equivalent stochastic structure which has the relation with stochastic variables. Statistical characteristics of equivalent stochastic structure can be obtained in accordance with mean square criterion, so nonlinear stochastic structure is transformed into linear stochastic structure. In order to attain that objective, the compound response spectrum of linear stochastic structure under stationary random excitation which is used in the solution is derived in the case of the mutual independence between stochastic excitation and stochastic structure. Finally, the example shows the accuracy and validity of the proposed method.
基金Project supported by the National Natural Science Foundation of China (No. 60874027)
文摘This paper investigates the robust stochastic stability and H∞ analysis for stochastic systems with time-varying delay and Markovian jump. By using the freeweighting matrix technique, i.e., He＇s technique, and a stochastic Lyapunov-Krasovskii functional, new delay-dependent criteria in terms of linear matrix inequalities are derived for the the robust stochastic stability and the H∞ disturbance attenuation. Three numerical examples axe given. The results show that the proposed method is efficient and much less conservative than the existing results in the literature.
文摘Utilizing the well-known aggregation technique, we propose a smoothing sample average approximation （SAA） method for a stochastic linear complementarity problem, where the underlying functions are represented by expectations of stochastic functions. The method is proved to be convergent and the preliminary numerical results are reported.
文摘In this paper, a class of the stochastic generalized linear complementarity problems with finitely many elements is proposed for the first time. Based on the Fischer-Burmeister function, a new conjugate gradient projection method is given for solving the stochastic generalized linear complementarity problems. The global convergence of the conjugate gradient projection method is proved and the related numerical results are also reported.
基金National Natural Science Foundation of China(No.11571373)
文摘In order to simulate a linear stochastic oscillator with additive noise,improved nonstandard optimal(INSOPT) schemes are derived utilizing the nonstandard finite difference(NSFD)technique and the improvement technique.These proposed schemes reproduce long time features of the oscillator solution exactly.Their abilities in preserving the symplecticity,the linear growth property of the second moment and the oscillation property of the solution of the stochastic oscillator system on long time interval are studied.It can be shown that the component { x_n}_(n≥1) of the INSOPT schemes switch signs infinitely many times as n →∞,almost surely.Further,the mean-square convergence order of 1 is obtained for these INSOPT schemes.Finally,numerical experiments illustrate intuitively the results obtained in this paper.
文摘A technique is developed for finding a closed form expression for the cumulative distribution function of the maximum value of the objective function in a stochastic linear programming problem, where either the objective function coefficients or the right hand side coefficients are continuous random vectors with known probability distributions. This is the “wait and see” problem of stochastic linear programming. Explicit results for the distribution problem are extremely difficult to obtain;indeed, previous results are known only if the right hand side coefficients have an exponential distribution [1]. To date, no explicit results have been obtained for stochastic c, and no new results of any form have appeared since the 1970’s. In this paper, we obtain the first results for stochastic c, and new explicit results if b an c are stochastic vectors with an exponential, gamma, uniform, or triangle distribution. A transformation is utilized that greatly reduces computational time.
基金The project supported by National Natural Science Foundation of China
文摘This paper investigates the random responses of a TDOF structure with strongly nonlinear coupling and parametric vibration. With the nonlinear cou- pling of inertia in the equations of motion of the system being removed by successive elimination, the non-Gaussian moment equation method (NGM) is applied and 69 moment equations are integrated with central cumulative truncation technique. The stochastic central difference-cum-statistical linearization method(SCD-SL) and the digital simulation method(DSM) are also used. A comparison of results by different methods are given and the SCD-SL method is the most efficient method.
文摘Modern financial theory, commonly known as portfolio theory, provides an analytical framework for the investment decision to be made under uncertainty. It is a well-established proposition in portfolio theory that whenever there is an imperfect correlation between returns risk is reduced by maintaining only a portion of wealth in any asset, or by selecting a portfolio according to expected returns and correlations between returns. The major improvement of the portfolio approaches over prior received theory is the incorporation of 1) the riskiness of an asset and 2) the addition from investing in any asset. The theme of this paper is to discuss how to propose a new mathematical model like that provided by Markowitz, which helps in choosing a nearly perfect portfolio and an efficient input/output. Besides applying this model to reality, the researcher uses game theory, stochastic and linear programming to provide the model proposed and then uses this model to select a perfect portfolio in the Cairo Stock Exchange. The results are fruitful and the researcher considers this model a new contribution to previous models.
基金financial support partly by the National Nature Science Foundation of China(Grant No.12171053,11701040,11871010&61871058)the Fundamental Research Funds for the Central Universities+2 种基金the Research Funds of Renmin University of China(No.23XNKJ05)the financial support partly by the National Nature Science Foundation of China(Grant No.11871010,11971040)the Fundamental Research Funds for the Central Universities(No.2019XD-A11).
文摘In this paper,we focus on a control-constrained stochastic LQ optimal control problem via backward stochastic differential equation(BSDE in short)with deterministic coefficients.One of the significant features in this framework,in contrast to the classical LQ issue,embodies that the admissible control set needs to satisfy more than the square integrability.By introducing two kinds of new generalized Riccati equations,we are able to announce the explicit optimal control and the solution to the corresponding H-J-B equation.A linear quadratic recursive utility portfolio optimization problem in the financial engineering is discussed as an explicitly illustrated example of the main result with short-selling prohibited.Feasibility of the mean-variance portfolio selection problem via BSDE for a financial market is characterized,and associated efficient portfolios are given in a closed form.
基金supported by the National Natural Science Foundation of China(Nos.11971334,12025105)。
文摘The author studies a stochastic linear quadratic(SLQ for short)optimal control problem for systems governed by stochastic evolution equations,where the control operator in the drift term may be unbounded.Under the condition that the cost functional is uniformly convex,the well-posedness of the operator-valued Riccati equation is proved.Based on that,the optimal feedback control of the control problem is given.
基金This work was supported by the National Basic Research Program of China (973 Program) under Grant No. 2007CB814904the Natural Science Foundation of China under Grant No. 10671112+1 种基金Shandong Province under Grant No. Z2006A01Research Fund for the Doctoral Program of Higher Education of China under Grant No. 20060422018
文摘In this paper, tile authors first study two kinds of stochastic differential equations （SDEs） with Levy processes as noise source. Based on the existence and uniqueness of the solutions of these SDEs and multi-dimensional backward stochastic differential equations （BSDEs） driven by Levy pro- cesses, the authors proceed to study a stochastic linear quadratic （LQ） optimal control problem with a Levy process, where the cost weighting matrices of the state and control are allowed to be indefinite. One kind of new stochastic Riccati equation that involves equality and inequality constraints is derived from the idea of square completion and its solvability is proved to be sufficient for the well-posedness and the existence of optimal control which can be of either state feedback or open-loop form of the LQ problems. Moreover, the authors obtain the existence and uniqueness of the solution to the Riccati equation for some special cases. Finally, two examples are presented to illustrate these theoretical results.
基金supported by the NSF of China under grants 11471231,11221101,11231007,11301298 and 11401404the PCSIRT under grant IRT 16R53 and the Chang Jiang Scholars Program from Chinese Education Ministry+1 种基金the Fundamental Research Funds for the Central Universities in China under grant 2015SCU04A02the NSFC-CNRS Joint Research Project under grant 11711530142。
文摘One of the fundamental issues in Control Theory is to design feedback controls.It is well-known that,the purpose of introducing Riccati equations in the study of deterministic linear quadratic control problems is exactly to construct the desired feedbacks.To date,the same problem in the stochastic setting is only partially well-understood.In this paper,we establish the equivalence between the existence of optimal feedback controls for the stochastic linear quadratic control problems with random coefficients and the solvability of the corresponding backward stochastic Riccati equations in a suitable sense.We also give a counterexample showing the nonexistence of feedback controls to a solvable stochastic linear quadratic control problem.This is a new phenomenon in the stochastic setting,significantly different from its deterministic counterpart.
基金This work was supported in part by the National Natural Science Foundation of China under Grant No.11801467the Chongqing Natural Science Foundation under Grant No.cstc2018jcyjAX0148.
文摘In this work,the author proposes a discretization for stochastic linear quadratic control problems(SLQ problems)subject to stochastic differential equations.The author firstly makes temporal discretization and obtains SLQ problems governed by stochastic difference equations.Then the author derives the convergence rates for this discretization relying on stochastic differential/difference Riccati equations.Finally an algorithm is presented.Compared with the existing results relying on stochastic Pontryagin-type maximum principle,the proposed scheme avoids solving backward stochastic differential equations and/or conditional expectations.
基金supported by the National Natural Science of China No.11971263,11871458Shandong Provincial Natural Science Foundation No.ZR2019ZD41National Key R&D Program of China No.2018YFA0703900。
文摘In this paper,a stochastic linear two-step scheme has been presented to approximate backward stochastic differential equations(BSDEs).A necessary and sufficient condition is given to judge the L 2-stability of our numerical schemes.This stochastic linear two-step method possesses a family of 3-order convergence schemes in the sense of strong stability.The coefficients in the numerical methods are inferred based on the constraints of strong stability and n-order accuracy(n∈N^(+)).Numerical experiments illustrate that the scheme is an efficient probabilistic numerical method.
文摘Using theory of Bayesian Dynamic Models and Forecasting , this paper mainly deals with the problem on state estimation for singular discrete time stochastic linear system. And a new method of state estimation linear Bayes estimation (LBE for short) has been proposed.
基金supported by the Major Key Project of Peng Cheng Laboratory under Grant No.PCL2023AS1-2Project funded by China Postdoctoral Science Foundation under Grant Nos.2022M722926 and2023T160605。
文摘In this paper,the authors consider a sparse parameter estimation problem in continuoustime linear stochastic regression models using sampling data.Based on the compressed sensing(CS)method,the authors propose a compressed least squares(LS) algorithm to deal with the challenges of parameter sparsity.At each sampling time instant,the proposed compressed LS algorithm first compresses the original high-dimensional regressor using a sensing matrix and obtains a low-dimensional LS estimate for the compressed unknown parameter.Then,the original high-dimensional sparse unknown parameter is recovered by a reconstruction method.By introducing a compressed excitation assumption and employing stochastic Lyapunov function and martingale estimate methods,the authors establish the performance analysis of the compressed LS algorithm under the condition on the sampling time interval without using independence or stationarity conditions on the system signals.At last,a simulation example is provided to verify the theoretical results by comparing the standard and the compressed LS algorithms for estimating a high-dimensional sparse unknown parameter.
基金supported by the National Natural Science Foundation of China(No.12031009)。
文摘The authors give a stochastic maximum principle for square-integrable optimal control of linear stochastic systems.The control domain is not necessarily convex and the cost functional can have a quadratic growth.In particular,they give a stochastic maximum principle for the linear quadratic optimal control problem.
文摘Based on the theory of Bayes forecasting, this paper mainly deals with the problem onthe state estimation for singular discrete-time stochastic linear system. And a new approach to optimalfiltering-linear Bayes estimation (LBE) has been proposed.
基金supported by the National Natural Science Foundation of China under Grant Nos.62103442,12326343,62373229the Research Grants Council of the Hong Kong Special Administrative Region,China under Grant Nos.CityU 11213023,11205724+3 种基金the Natural Science Foundation of Shandong Province under Grant No.ZR2021QF080the Taishan Scholar Project of Shandong Province under Grant No.tsqn202408110the Fundamental Research Foundation of the Central Universities under Grant No.23CX06024Athe Outstanding Youth Innovation Team in Shandong Higher Education Institutions under Grant No.2023KJ061.
文摘This paper reviews the mean field social(MFS)optimal control problem for multi-agent dynamic systems and the mean-field-type(MFT)optimal control problem for single-agent dynamic systems within the linear quadratic(LQ)framework.For the MFS control problem,this review discusses the existing conclusions on optimization in dynamic systems affected by both additive and multiplicative noises.In exploring MFT optimization,the authors first revisit researches associated with single-player systems constrained by these dynamics.The authors then extend the proposed review to scenarios that include multiple players engaged in Nash games,Stackelberg games,and cooperative Pareto games.Finally,the paper concludes by emphasizing future research on intelligent algorithms for mean field optimization,particularly using reinforcement learning method to design strategies for models with unknown parameters.
