RSS Feed for dynamic programming and optimal control https://solvonauts.org/%3Faction%3Drss_search%26term%3Ddynamic+programming+and+optimal+control RSS Feed for dynamic programming and optimal control 6.231 Dynamic Programming and Stochastic Control (MIT) 6.231 Dynamic Programming and Stochastic Control (MIT) This course covers the basic models and solution techniques for problems of sequential decision making under uncertainty stochastic control We will consider optimal control of a dynamical system over both a finite and an infinite number of stages finite and infinite horizon We will also discuss some approximation methods for problems involving large state spaces Applications of dynamic programming in a variety of fields will be covered in recitations This course covers the basic models and solution techniques for problems of sequential decision making under uncertainty stochastic control We will consider optimal control of a dynamical system over both a finite and an infinite number of stages finite and infinite horizon We will also discuss some approximation methods for problems involving large state spaces Applications of dynamic programming in a variety of fields will be covered in recitations http://dspace.mit.edu/handle/1721.1/75813 http://dspace.mit.edu/handle/1721.1/75813 15.093 Optimization Methods (SMA 5213) (MIT) 15.093 Optimization Methods (SMA 5213) (MIT) This course introduces the principal algorithms for linear network discrete nonlinear dynamic optimization and optimal control Emphasis is on methodology and the underlying mathematical structures Topics include the simplex method network flow methods branch and bound and cutting plane methods for discrete optimization optimality conditions for nonlinear optimization interior point methods for convex optimization Newton s method heuristic methods and dynamic programming and optimal control methods This course was also taught as part of the Singapore MIT Alliance SMA programme as course number SMA 5213 Optimisation Methods This course introduces the principal algorithms for linear network discrete nonlinear dynamic optimization and optimal control Emphasis is on methodology and the underlying mathematical structures Topics include the simplex method network flow methods branch and bound and cutting plane methods for discrete optimization optimality conditions for nonlinear optimization interior point methods for convex optimization Newton s method heuristic methods and dynamic programming and optimal control methods This course was also taught as part of the Singapore MIT Alliance SMA programme as course number SMA 5213 Optimisation Methods http://dspace.mit.edu/handle/1721.1/67658 http://dspace.mit.edu/handle/1721.1/67658 14.451 Dynamic Optimization Methods with Applications (MIT) 14.451 Dynamic Optimization Methods with Applications (MIT) This course focuses on dynamic optimization methods both in discrete and in continuous time We approach these problems from a dynamic programming and optimal control perspective We also study the dynamic systems that come from the solutions to these problems The course will illustrate how these techniques are useful in various applications drawing on many economic examples However the focus will remain on gaining a general command of the tools so that they can be applied later in other classes This course focuses on dynamic optimization methods both in discrete and in continuous time We approach these problems from a dynamic programming and optimal control perspective We also study the dynamic systems that come from the solutions to these problems The course will illustrate how these techniques are useful in various applications drawing on many economic examples However the focus will remain on gaining a general command of the tools so that they can be applied later in other classes http://ocw.mit.edu/courses/economics/14-451-dynamic-optimization-methods-with-applications-fall-2009 http://ocw.mit.edu/courses/economics/14-451-dynamic-optimization-methods-with-applications-fall-2009 15.093J Optimization Methods (MIT) 15.093J Optimization Methods (MIT) This course introduces the principal algorithms for linear network discrete nonlinear dynamic optimization and optimal control Emphasis is on methodology and the underlying mathematical structures Topics include the simplex method network flow methods branch and bound and cutting plane methods for discrete optimization optimality conditions for nonlinear optimization interior point methods for convex optimization Newton s method heuristic methods and dynamic programming and optimal control methods This course introduces the principal algorithms for linear network discrete nonlinear dynamic optimization and optimal control Emphasis is on methodology and the underlying mathematical structures Topics include the simplex method network flow methods branch and bound and cutting plane methods for discrete optimization optimality conditions for nonlinear optimization interior point methods for convex optimization Newton s method heuristic methods and dynamic programming and optimal control methods http://ocw.mit.edu/courses/sloan-school-of-management/15-093j-optimization-methods-fall-2009 http://ocw.mit.edu/courses/sloan-school-of-management/15-093j-optimization-methods-fall-2009 6.231 Dynamic Programming and Stochastic Control (MIT) This course covers the basic models and solution techniques for problems of sequential decision making under uncertainty stochastic control We will consider optimal control of a dynamical system over both a finite and an infinite number of stages finite and infinite horizon We will also discuss some approximation methods for problems involving large state spaces Applications of dynamic programming in a variety of fields will be covered in recitations https://dspace.mit.edu/handle/1721.1/75813 https://dspace.mit.edu/handle/1721.1/75813 15.093 Optimization Methods (SMA 5213) (MIT) This course introduces the principal algorithms for linear network discrete nonlinear dynamic optimization and optimal control Emphasis is on methodology and the underlying mathematical structures Topics include the simplex method network flow methods branch and bound and cutting plane methods for discrete optimization optimality conditions for nonlinear optimization interior point methods for convex optimization Newton s method heuristic methods and dynamic programming and optimal control methods This course was also taught as part of the Singapore MIT Alliance SMA programme as course number SMA 5213 Optimisation Methods https://dspace.mit.edu/handle/1721.1/67658 https://dspace.mit.edu/handle/1721.1/67658 14.451 Dynamic Optimization Methods with Applications (MIT) This course focuses on dynamic optimization methods both in discrete and in continuous time We approach these problems from a dynamic programming and optimal control perspective We also study the dynamic systems that come from the solutions to these problems The course will illustrate how these techniques are useful in various applications drawing on many economic examples However the focus will remain on gaining a general command of the tools so that they can be applied later in other classes https://ocw.mit.edu/courses/economics/14-451-dynamic-optimization-methods-with-applications-fall-2009 https://ocw.mit.edu/courses/economics/14-451-dynamic-optimization-methods-with-applications-fall-2009 15.093J Optimization Methods (MIT) This course introduces the principal algorithms for linear network discrete nonlinear dynamic optimization and optimal control Emphasis is on methodology and the underlying mathematical structures Topics include the simplex method network flow methods branch and bound and cutting plane methods for discrete optimization optimality conditions for nonlinear optimization interior point methods for convex optimization Newton s method heuristic methods and dynamic programming and optimal control methods https://ocw.mit.edu/courses/sloan-school-of-management/15-093j-optimization-methods-fall-2009 https://ocw.mit.edu/courses/sloan-school-of-management/15-093j-optimization-methods-fall-2009