Variation principle - holonomic constraints

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SUMMARY

The discussion focuses on extending Hamilton's principle to include holonomic constraints by incorporating Lagrange multipliers. The key equation presented is ∑fa*λa = 0, where fa represents the constraint equations. By treating the Lagrange multipliers (λa) as independent coordinates, the original Euler-Lagrange equations can be derived while allowing variations of other coordinates (δqi) without considering the constraints. This method effectively maintains the degrees of freedom in an optimization problem by balancing the constraints with the added multipliers.

PREREQUISITES
  • Understanding of Hamilton's principle
  • Familiarity with Lagrangian mechanics
  • Knowledge of Lagrange multipliers
  • Basic calculus concepts related to optimization
NEXT STEPS
  • Study the derivation of the Euler-Lagrange equations in constrained systems
  • Explore advanced applications of Lagrange multipliers in optimization problems
  • Investigate the relationship between gradients of constraints and objective functions
  • Learn about holonomic vs. non-holonomic constraints in mechanics
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Students and professionals in physics, particularly those studying classical mechanics and optimization techniques, will benefit from this discussion.

QuasarBoy543298
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in order to extend hamilton's principle to include holonomic constraints, out lecturer did the following :
when we are under constraints, we cannot consider the variations of the coordinates as independent of each other.
we know that the constraint equations are fa = 0.
we can multiply each constraint by an arbitrary constant λa.
∑faa = 0, so we can just add it to the Lagrangian, as we treat the λa
as independent coordinates.

from there we just followed the same thing we did in order to find the original EL equations.my question is - why can we treat the λa as independent coordinates.
also, why doing so gives us the option to treat the other coordinates as independent from each other (vary them by δqi without considering the constraints).

besides, if someone can explain a little the background of this process because to me it feels like we just added something that relates to the constraints into the Lagrangian and got new equations plus the original constraints.thanks in advance !
 
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Did you already study Lagrange multipliers in calculus ?
It basically has to do with the two gradients (of the constraint(s) and of the objective function to be minimized): they have to be collinear.
You remove (number of constraints) degrees of freedom from an n##\times##n problem, so you add n ##\lambda##'s to come back to an n##\times##n optimization problem again.
 

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