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"Lagrange multipliers without permanent scarring] Explanation with focus on the intuition" (PDF). nlp.cs.berkeley.edu. University of California, Berkeley. Sathyanarayana, Shashi. "Geometric representation of method of Lagrange multipliers". wolfram.com (Mathematica demonstration). Wolfram Research. Needs Internet Explorer / Firefox / Safari.
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 , are Lagrange multipliers imposing constraints, such as local rigid body deformations. To ensure that dissipation occurs only through the Υ {\displaystyle \Upsilon } coupling and not as a consequence of the interconversion by the operators Γ , Λ {\displaystyle \Gamma ,\Lambda } the following adjoint conditions are imposed
The method penalizes violations of inequality constraints using a Lagrange multiplier, which imposes a cost on violations. These added costs are used instead of the strict inequality constraints in the optimization. In practice, this relaxed problem can often be solved more easily than the original problem.
A third approach is to use a method such as Lagrange multipliers or projection to the constraint manifold to determine the coordinate adjustments necessary to satisfy the constraints. Finally, there are various hybrid approaches in which different sets of constraints are satisfied by different methods, e.g., internal coordinates, explicit ...
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 ...
with v the Lagrange multipliers on the non-negativity constraints, λ the multipliers on the inequality constraints, and s the slack variables for the inequality constraints. The fourth condition derives from the complementarity of each group of variables (x, s) with its set of KKT vectors (optimal Lagrange multipliers) being (v, λ). In that case,
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.