Optimisation Lagrangian Problem

AI Thread Summary
The discussion focuses on applying Lagrangian optimization to a problem with constraints. The original poster is unsure how to incorporate the variables px and qy into the Lagrangian framework. A suggestion is made to refer to Lagrange multiplier theory, which is relevant for maximizing a function subject to a constraint. Clarification is provided that px represents a constant p multiplied by the variable x, which should simplify the understanding of the problem. Overall, the conversation emphasizes the importance of grasping the fundamentals of two-variable optimization and the use of Lagrange multipliers.
JoshMaths
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No this is not homework.

http://imgur.com/zAZxmuC
http://imgur.com/zAZxmuC

Ok i am struggling to even start this question.

I see it has a constraint so i would be tempted to use Lagrangian but from there i don't see how px and qy fit into it?

Some assistance on the tools needed to approach this question would be great, it may be two-variable optimisation but i don't really have a handle on that either.

Thanks,

Josh
 
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It looks like Lagrange multiplier theory would come in handy. Look at the intro to this Wikipedia page...

For instance (see Figure 1), consider the optimization problem
maximize ##f(x, y)##
subject to ##g(x, y) = c##
That is exactly your problem (for suitable f, g and c).

Note that px is just one of the given constants p multiplied by the variable x, I'm not sure why that confuses you.
 
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