Search results
Results From The WOW.Com Content Network
The Lagrange multiplier theorem states that at any local maximum (or minimum) of the function evaluated under the equality constraints, if constraint qualification applies (explained below), then the gradient of the function (at that point) can be expressed as a linear combination of the gradients of the constraints (at that point), with the ...
where is a Lagrange multiplier or adjoint state variable and , is an inner product on . The method of Lagrange multipliers states that a solution to the problem has to be a stationary point of the lagrangian, namely
Several methods have been developed to impose the essential boundary conditions weakly, including Lagrange multipliers, Nitche's method, and the penalty method. As for quadrature , nodal integration is generally preferred which offers simplicity, efficiency, and keeps the meshfree method free of any mesh (as opposed to using Gauss quadrature ...
The costate variables () can be interpreted as Lagrange multipliers associated with the state equations. The state equations represent constraints of the minimization problem, and the costate variables represent the marginal cost of violating those constraints; in economic terms the costate variables are the shadow prices.
For the case of a conservative force given by the gradient of some potential energy V, a function of the r k coordinates only, substituting the Lagrangian L = T − V gives ˙ ⏟ + ⏟ + = =, and identifying the derivatives of kinetic energy as the (negative of the) resultant force, and the derivatives of the potential equaling the non ...
The Lagrange multiplier is related to a constraint condition = and usually represents a force or a moment, which acts in “direction” of the constraint degree of freedom. The Lagrange multipliers do no "work" as compared to external forces that change the potential energy of a body.
In the field of calculus of variations in mathematics, the method of Lagrange multipliers on Banach spaces can be used to solve certain infinite-dimensional constrained optimization problems. The method is a generalization of the classical method of Lagrange multipliers as used to find extrema of a function of finitely many variables.
The constraints on the system dynamics can be adjoined to the Lagrangian by introducing time-varying Lagrange multiplier vector , whose elements are called the costates of the system. This motivates the construction of the Hamiltonian H {\displaystyle H} defined for all t ∈ [ 0 , T ] {\displaystyle t\in [0,T]} by: