Minimization - optimization alg. or equation alg.?

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SUMMARY

This discussion focuses on the optimization of numerical algorithms, specifically addressing the minimization problem defined as min w = f(x) subject to g1(x)=0 through gn(x)=0, where x is a 25-dimensional vector and there are 2 constraints. The participants emphasize the choice between using the Lagrangian method to solve the resulting system of 27 nonlinear equations or implementing a direct minimization algorithm. The consensus suggests that if the constraints are known to be linear, alternative approaches may be more suitable, but in the absence of such information, Lagrange multipliers are recommended as an effective method.

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sodemus
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Hello everybody!
I guess my question is mainly concerned with numerical algorithms...
Given a problem of the form
min w = f(x)
subject to
g1(x)=0
:
:
gn(x)=0
where x is a m x 1 vector, n < m.

From a numerical standpoint, how can I know whether it is preferably to solve it by setting up the Lagrangian and solve the resulting system of m + n non linear equations with appropriate algorithms OR to implement an appropriate algorithm to solve the minimization problem directly? As far as my particular problem goes, let's say n = 2 and m = 25.

Any help is more than appreciated!
 
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If you had knowledge about the constraints, e.g. linearity, you could chose another approach. Without any further information, Lagrange multipliers should be fine.
 

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