Applications of Stochastic Optimal Control to Economics and Finance
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Propt Product Sheet
PROPT - The world’s fastest Optimal Control platform for MATLAB. PROPT - ONE OF A KIND, LIGHTNING FAST SOLUTIONS TO YOUR OPTIMAL CONTROL PROBLEMS! NOW WITH WELL OVER 100 TEST CASES! The PROPT software package is intended to When using PROPT, optimally coded analytical solve dynamic optimization problems. Such first and second order derivatives, including problems are usually described by: problem sparsity patterns are automatically generated, thereby making it the first MATLAB • A state-space model of package to be able to fully utilize a system. This can be NLP (and QP) solvers such as: either a set of ordinary KNITRO, CONOPT, SNOPT and differential equations CPLEX. (ODE) or differential PROPT currently uses Gauss or algebraic equations (PAE). Chebyshev-point collocation • Initial and/or final for solving optimal control conditions (sometimes problems. However, the code is also conditions at other written in a more general way, points). allowing for a DAE rather than an ODE formulation. Parameter • A cost functional, i.e. a estimation problems are also scalar value that depends possible to solve. on the state trajectories and the control function. PROPT has three main functions: • Sometimes, additional equations and variables • Computation of the that, for example, relate constant matrices used for the the initial and final differentiation and integration conditions to each other. of the polynomials used to approximate the solution to the trajectory optimization problem. The goal of PROPT is to make it possible to input such problem • Source transformation to turn descriptions as simply as user-supplied expressions into possible, without having to worry optimized MATLAB code for the about the mathematics of the cost function f and constraint actual solver. -
STOCHASTIC CONTROL Contents 1. Introduction 1 2. Mathematical Probability 1 3. Stochastic Calculus 3 4. Diffusions 7 5. the Stoc
STOCHASTIC CONTROL NAFTALI HARRIS Abstract. In this paper we discuss the Stochastic Control Problem, which deals with the best way to control a stochastic system and has applications in fields as diverse as robotics, finance, and industry. After quickly reviewing mathematical probability and stochastic integration, we prove the Hamilton- Jacobi-Bellman equations for stochastic control, which transform the Stochas- tic Control Problem into a partial differential equation. Contents 1. Introduction 1 2. Mathematical Probability 1 3. Stochastic Calculus 3 4. Diffusions 7 5. The Stochastic Control Problem 11 6. The Hamilton-Jacobi-Bellman Equation 12 7. Acknowledgements 18 References 18 1. Introduction The Stochastic Control Problem arises when we wish to control a continuous- time random system optimally. For example, we wish to fly an airplane through turbulence, or manage a stock portfolio, or precisely maintain the temperature and pressure of a system in the presence of random air currents and temperatures. To build up to a formal definition of this problem, we will first review mathematical probability and stochastic calculus. These will give us the tools to talk about It^o processes and diffusions, which will allow us to state the Stochastic Control Problem formally. Finally, we will spend the latter half of this paper proving necessary conditions and sufficient conditions for its solution. 2. Mathematical Probability In this section we briskly define the mathematical model of probability required in the rest of this paper. We define probability spaces, random variables, and conditional expectation. For more detail, see Williams [4]. Definition 2.1 (Probability Space). A probability space is a triple (Ω; F;P ), where Ω is an arbitrary set, F is a sigma algebra over Ω, and P is a measure on sets in F with P (Ω) = 1. -
Optimal Control Theory Version 0.2
An Introduction to Mathematical Optimal Control Theory Version 0.2 By Lawrence C. Evans Department of Mathematics University of California, Berkeley Chapter 1: Introduction Chapter 2: Controllability, bang-bang principle Chapter 3: Linear time-optimal control Chapter 4: The Pontryagin Maximum Principle Chapter 5: Dynamic programming Chapter 6: Game theory Chapter 7: Introduction to stochastic control theory Appendix: Proofs of the Pontryagin Maximum Principle Exercises References 1 PREFACE These notes build upon a course I taught at the University of Maryland during the fall of 1983. My great thanks go to Martino Bardi, who took careful notes, saved them all these years and recently mailed them to me. Faye Yeager typed up his notes into a first draft of these lectures as they now appear. Scott Armstrong read over the notes and suggested many improvements: thanks, Scott. Stephen Moye of the American Math Society helped me a lot with AMSTeX versus LaTeX issues. My thanks also to Atilla Yilmaz for spotting lots of typos and errors, which I have corrected. I have radically modified much of the notation (to be consistent with my other writings), updated the references, added several new examples, and provided a proof of the Pontryagin Maximum Principle. As this is a course for undergraduates, I have dispensed in certain proofs with various measurability and continuity issues, and as compensation have added various critiques as to the lack of total rigor. This current version of the notes is not yet complete, but meets I think the usual high standards for material posted on the internet. Please email me at [email protected] with any corrections or comments. -
MARKOV DECISION PROCESSES Abstract: the Theory of Markov
July 06, 2010, 15:00 Draft, don’t distribute MARKOV DECISION PROCESSES NICOLE BAUERLE¨ ∗ AND ULRICH RIEDER‡ Abstract: The theory of Markov Decision Processes is the theory of controlled Markov chains. Its origins can be traced back to R. Bellman and L. Shapley in the 1950’s. During the decades of the last century this theory has grown dramatically. It has found applications in various areas like e.g. computer science, engineering, operations research, biology and economics. In this article we give a short intro- duction to parts of this theory. We treat Markov Decision Processes with finite and infinite time horizon where we will restrict the presentation to the so-called (gen- eralized) negative case. Solution algorithms like Howard’s policy improvement and linear programming are also explained. Various examples show the application of the theory. We treat stochastic linear-quadratic control problems, bandit problems and dividend pay-out problems. AMS 2010 Classification: 90C40, 60J05, 93E20 Keywords and Phrases: Markov Decision Process, Markov Chain, Bell- man Equation, Policy Improvement, Linear Programming 1. Introduction Do you want to play a card game? Yes? Then I will tell you how it works. We have a well-shuffled standard 32-card deck which is also known as a piquet deck. 16 cards are red and 16 cards are black. Initially the card deck lies on the table face down. Then I start to remove the cards and you are able to see its faces. Once you have to say ”stop”. If the next card is black you win 10 Euro, if it is red you loose 10 Euro. -
On Some Recent Aspects of Stochastic Control and Their Applications 507
Probability Surveys Vol. 2 (2005) 506–549 ISSN: 1549-5787 DOI: 10.1214/154957805100000195 On some recent aspects of stochastic control and their applications∗ Huyˆen Pham† Laboratoire de Probabilit´es et Mod`eles Al´eatoires, CNRS, UMR 7599 Universit´eParis 7, e-mail: [email protected], and CREST Abstract: This paper is a survey on some recent aspects and developments in stochastic control. We discuss the two main historical approaches, Bell- man’s optimality principle and Pontryagin’s maximum principle, and their modern exposition with viscosity solutions and backward stochastic differ- ential equations. Some original proofs are presented in a unifying context including degenerate singular control problems. We emphasize key results on characterization of optimal control for diffusion processes, with a view towards applications. Some examples in finance are detailed with their ex- plicit solutions. We also discuss numerical issues and open questions. AMS 2000 subject classifications: 93E20, 49J20, 49L20, 60H30. Keywords and phrases: Controlled diffusions, dynamic programming, maximum principle, viscosity solutions, backward stochastic differential equations, finance. Received December 2005. arXiv:math/0509711v3 [math.PR] 12 Jan 2006 ∗This is an original survey paper †I would like to thank Philip Protter for his encouragement to write this paper. 506 H. Pham/On some recent aspects of stochastic control and their applications 507 Contents 1 Introduction 508 2 Theproblemanddiscussiononmethodology 508 2.1 Problemformulation . 508 2.2 Bellman’s optimality principle . 509 2.2.1 The Hamilton-Jacobi-Bellman (in)equation . 511 2.2.2 Theclassicalverificationapproach . 512 2.3 Pontryagin’s maximum principle . 514 2.4 Othercontrolproblems . 515 2.4.1 Randomhorizon . -
Ensemble Kalman Filtering for Inverse Optimal Control Andrea Arnold and Hien Tran, Member, SIAM
Ensemble Kalman Filtering for Inverse Optimal Control Andrea Arnold and Hien Tran, Member, SIAM Abstract—Solving the inverse optimal control problem for In the Bayesian framework, unknown parameters are mod- discrete-time nonlinear systems requires the construction of a eled as random variables with probability density functions stabilizing feedback control law based on a control Lyapunov representing distributions of possible values. The EnKF is a function (CLF). However, there are few systematic approaches available for defining appropriate CLFs. We propose an ap- nonlinear Bayesian filter which uses ensemble statistics in proach that employs Bayesian filtering methodology to param- combination with the classical Kalman filter equations for eterize a quadratic CLF. In particular, we use the ensemble state and parameter estimation [9]–[11]. The EnKF has been Kalman filter (EnKF) to estimate parameters used in defining employed in many settings, including weather prediction the CLF within the control loop of the inverse optimal control [12], [13] and mathematical biology [11]. To the authors’ problem formulation. Using the EnKF in this setting provides a natural link between uncertainty quantification and optimal knowledge, this is the first proposed use of the EnKF in design and control, as well as a novel and intuitive way to find inverse optimal control problems. The novelty of using the the one control out of an ensemble that stabilizes the system the EnKF in this setting allows us to generate an ensemble of fastest. Results are demonstrated on both a linear and nonlinear control laws, from which we can then select the control law test problem. that drives the system to zero the fastest. -
Stochastic Control
1 Stochastic Control Roger Brockett April 3, 2009 2 PREFACE Teaching stochastic processes to students whose primary interests are in applications has long been a problem. On one hand, the subject can quickly become highly technical and if mathematical concerns are allowed to dominate there may be no time available for exploring the many interesting areas of applications. On the other hand, the treatment of stochastic calculus in a cavalier fashion leaves the student with a feeling of great uncertainty when it comes to exploring new material. Moreover, the problem has become more acute as the power of the differential equation point of view has become more widely appreciated. In these notes, an attempt is made to resolve this dilemma with the needs of those interested in building models and designing algorithms for estimation and control in mind. The approach is to start with Poisson counters and to identify the Wiener process with a certain limiting form. We do not attempt to define the Wiener process per se. Instead, everything is done in terms of limits of jump processes. The Poisson counter and differential equations whose right-hand sides include the differential of Poisson counters are developed first. This leads to the construction of a sample path representations of a continuous time jump process using Poisson counters. This point of view leads to an efficient problem solving technique and permits a unified treatment of time varying and nonlinear problems. More importantly, it provides sound intuition for stochastic differential equations and their uses without allowing the technicalities to dominate. In treating estimation theory, the conditional density equa- tion is given a central role. -
Optimal Control Theory
Optimal Control Theory Mrinal Kanti Ghosh Department of Mathematics Indian Institute of Science Bangalore 560 012 email : [email protected] 1 Introduction Optimal Control Theory deals with optimization problems involving a controlled dynamical system. A controlled dynamical system is a dynamical system in which the trajectory can be altered continuously in time by choosing a control parameter u(t) continuously in time. A (deterministic) controlled dynamical system is usually governed by an ordinary differential equation of the form 9 x_(t) = f(t; x(t); u(t)); t > 0 = (1:1) d ; x(0) = x0 2 R where u : Rd ! U is a function called the control, U a given set called the control set, and d d f : R+ × R × U ! IR : By choosing the value of u(t), the state trajectory x(t) can be controlled. The objective of controlling the state trajectory is to minimize a certain cost associated with (1.1). Consider the following important problem studied by Bushaw and Lasalle in the field of aerospace engineering. Let x(t) represent the deviation of a rocket trajectory from some desired flight path. Then, provided the deviation is small, it is governed to a good approxi- mation by 9 x_(t) = A(t)x(t) + B(t)u(t); t > 0 = x(0) = x0; x(T ) = 0; ; (1:2) u(t) 2 [−1; 1]; where A and B are appropriate matrix valued functions. The vector x0 is the initial deviation, u(t) is the rocket thrust at time t, and T is the final time. -
PSO-Based Soft Lunar Landing with Hazard Avoidance: Analysis and Experimentation
aerospace Article PSO-Based Soft Lunar Landing with Hazard Avoidance: Analysis and Experimentation Andrea D’Ambrosio 1,* , Andrea Carbone 1 , Dario Spiller 2 and Fabio Curti 1 1 School of Aerospace Engineering, Sapienza University of Rome, Via Salaria 851, 00138 Rome, Italy; [email protected] (A.C.); [email protected] (F.C.) 2 Italian Space Agency, Via del Politecnico snc, 00133 Rome, Italy; [email protected] * Correspondence: [email protected] Abstract: The problem of real-time optimal guidance is extremely important for successful au- tonomous missions. In this paper, the last phases of autonomous lunar landing trajectories are addressed. The proposed guidance is based on the Particle Swarm Optimization, and the differ- ential flatness approach, which is a subclass of the inverse dynamics technique. The trajectory is approximated by polynomials and the control policy is obtained in an analytical closed form solution, where boundary and dynamical constraints are a priori satisfied. Although this procedure leads to sub-optimal solutions, it results in beng fast and thus potentially suitable to be used for real-time purposes. Moreover, the presence of craters on the lunar terrain is considered; therefore, hazard detection and avoidance are also carried out. The proposed guidance is tested by Monte Carlo simulations to evaluate its performances and a robust procedure, made up of safe additional maneuvers, is introduced to counteract optimization failures and achieve soft landing. Finally, the whole procedure is tested through an experimental facility, consisting of a robotic manipulator, equipped with a camera, and a simulated lunar terrain. The results show the efficiency and reliability Citation: D’Ambrosio, A.; Carbone, of the proposed guidance and its possible use for real-time sub-optimal trajectory generation within A.; Spiller, D.; Curti, F. -
ID 467 Optimal Control Applied to an Economic Model for Reducing Unemployment Problems in Bangladesh
Proceedings of the International Conference on Industrial Engineering and Operations Management Bangkok, Thailand, March 5-7, 2019 Optimal Control Applied to an Economic Model for Reducing Unemployment Problems in Bangladesh Md. Haider Ali Biswas, Uzzwal Kumar Mallick, Mst. Shanta Khatun Mathematics Discipline, Khulna University Khulna-9208, Bangladesh [email protected],[email protected],[email protected] Kazi Masum Parves Joint Director, Bangladesh Bank, Khulna, Bangladesh [email protected] Abstract In this paper, a nonlinear mathematical model of unemployment is described and analyzed for reduction of unemployed persons using two controls ‘creation of new vacancies’ and ‘skilled manpower’. For maximization of the number of employed people at the end of implemented policy, we find the control profile of two controls using Pontryagin’s Maximum Principle. This study concludes two effective controls so that the number of unemployed persons at the end of implemented policies of the government is minimized while minimizing the systematic cost of policy making. Our numerical results as illustrated in the figures strongly agree with the analytical findings. Keywords: Unemployment, Socio-economic damage, Mathematical model, Optimal control, Numerical simulation. 1. Introduction Unemployment problems have become the most immense concerns all over the world. This issue is significantly an alarming concern in Bangladesh as well. Unemployment can be defined as a state of workless for a person, who is fit, capable and willing to work. It is well known that the main reason for socio-economic damage, deletion of morality and social values is unemployment. With the increasing tendency of unemployed population in a country significantly affects the other factors, such that the income per person, health costs, quality of healthcare and poverty and seriously hamper the growth of national economy. -
Optimal Control the Optimal Control Problem Can Be Described By
Optimal Control The optimal control problem can be described by introducing the system dynamics x_ = F (x; u) which is assumed to start in an initial state given by x(0) = x0 and has controllable parameters u u 2 U The objective function consists of a function of the final state [x(T )] and a cost function (or loss function) ` that is integrated over time. Z T J = [x(T )] + `(u; x)dt 0 1 The simplest optimization problem is depicted in Figure 1: min F~(x) x 40 4 20 2 0 0 -4 -2 -2 0 2 -4 4 Figure 1: A simple case for optimization: a function of two variables has a single minimum at the point x∗. 2 Constraints The simplest kind of constraint is an equality constraint, for example, g(x) = 0. Formally this addition is stated as min F~(x) subject to G(x) = 0 x This complication can be handled by the method of Lagrange. This method reduces the constrained problem to a new, unconstrained minimization prob- lem with additional variables. The additional variables are known as Lagrange multipliers. To handle this problem, add g(x) to f~(x) using a Lagrange mul- tiplier λ: F (x; λ) = F~(x) + λG(x) The Lagrange multiplier is an extra scalar variable, so the number of degrees of freedom of the problem has increased, but the advantage is that now sim- ple, unconstrained minimization techniques can be applied to the composite function. The problem becomes min F (x; λ) x,λ Most often the equality constraint is the dynamic equations of the system itself, x_ = F (x; u), and the problem is to find an optimal trajectory x(t). -
Optimal and Autonomous Control Using Reinforcement Learning: a Survey
2042 IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, VOL. 29, NO. 6, JUNE 2018 Optimal and Autonomous Control Using Reinforcement Learning: A Survey Bahare Kiumarsi , Member, IEEE , Kyriakos G. Vamvoudakis, Senior Member, IEEE , Hamidreza Modares , Member, IEEE , and Frank L. Lewis, Fellow, IEEE Abstract — This paper reviews the current state of the art on unsupervised, or reinforcement, depending on the amount and reinforcement learning (RL)-based feedback control solutions quality of feedback about the system or task. In supervised to optimal regulation and tracking of single and multiagent learning, the feedback information provided to learning algo- systems. Existing RL solutions to both optimal H2 and H∞ control problems, as well as graphical games, will be reviewed. rithms is a labeled training data set, and the objective is RL methods learn the solution to optimal control and game to build the system model representing the learned relation problems online and using measured data along the system between the input, output, and system parameters. In unsu- trajectories. We discuss Q-learning and the integral RL algorithm pervised learning, no feedback information is provided to the as core algorithms for discrete-time (DT) and continuous-time algorithm and the objective is to classify the sample sets to (CT) systems, respectively. Moreover, we discuss a new direction of off-policy RL for both CT and DT systems. Finally, we review different groups based on the similarity between the input sam- several applications. ples. Finally, reinforcement learning (RL) is a goal-oriented Index Terms — Autonomy, data-based optimization, learning tool wherein the agent or decision maker learns a reinforcement learning (RL).