The Hamiltonian is a function used to solve a problem of optimal control for a dynamical system. It can be understood as an instantaneous increment of the Lagrangian expression of the problem that is to be optimized over a certain time period. Inspired by—but distinct from—the Hamiltonian of classical mechanics, the Hamiltonian of optimal control theory was developed by Lev Pontryagin as part of his maximum principle. Pontryagin proved that a necessary condition for solving the optimal control problem is that the control should be chosen so as to optimize the Hamiltonian.
Consider a dynamical system of first-order differential equations
where denotes a vector of state variables, and a vector of control variables. Once initial conditions and controls are specified, a solution to the differential equations, called a trajectory , can be found. The problem of optimal control is to choose (from some set ) so that maximizes or minimizes a certain objective function between an initial time and a terminal time (where may be infinity). Specifically, the goal is to optimize a performance index at each point in time,
subject to the above equations of motion of the state variables. The solution method involves defining an ancillary function known as the control Hamiltonian
which combines the objective function and the state equations much like a Lagrangian in a static optimization problem, only that the multipliers —referred to as costate variables—are functions of time rather than constants.
The goal is to find an optimal control policy function and, with it, an optimal trajectory of the state variable , which by Pontryagin's maximum principle are the arguments that maximize the Hamiltonian,
for all
The first-order necessary conditions for a maximum are given by
which is the maximum principle,
which generates the state transition function ,
which generates
the latter of which are referred to as the costate equations.
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A shadow price is the monetary value assigned to an abstract or intangible commodity which is not traded in the marketplace. This often takes the form of an externality. Shadow prices are also known as the recalculation of known market prices in order to account for the presence of distortionary market instruments (e.g. quotas, tariffs, taxes or subsidies). Shadow prices are the real economic prices given to goods and services after they have been appropriately adjusted by removing distortionary market instruments and incorporating the societal impact of the respective good or service.
Pontryagin's maximum principle is used in optimal control theory to find the best possible control for taking a dynamical system from one state to another, especially in the presence of constraints for the state or input controls. It states that it is necessary for any optimal control along with the optimal state trajectory to solve the so-called Hamiltonian system, which is a two-point boundary value problem, plus a maximum condition of the control Hamiltonian.
Optimal control theory is a branch of mathematical optimization that deals with finding a control for a dynamical system over a period of time such that an objective function is optimized. It has numerous applications in science, engineering and operations research. For example, the dynamical system might be a spacecraft with controls corresponding to rocket thrusters, and the objective might be to reach the moon with minimum fuel expenditure.
This doctoral course provides an introduction to optimal control covering fundamental theory, numerical implementation and problem formulation for applications.
Discrete mathematics is a discipline with applications to almost all areas of study. It provides a set of indispensable tools to computer science in particular. This course reviews (familiar) topics a
L'étudiant apprendra à résoudre numériquement divers problèmes mathématiques. Les propriétés théoriques de ces
méthodes seront discutées.
Covers the fundamentals of optimal control theory, focusing on defining OCPs, existence of solutions, performance criteria, physical constraints, and the principle of optimality.
Covers optimal control, focusing on problems 4 and 7.
Covers optimization algorithms, stable matching, and Big-O notation for algorithm efficiency.
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