Given an NLO reduce it to unconstrained optimization problem

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In summary: I'd be surprised if anyone at this site has heard of this guy, let alone would know what DFP stands for.
  • #1
ver_mathstats
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Homework Statement
We are required to reduce an unconstrained optimization problem. Problem is written below.
Relevant Equations
reduce to an unconstrained optimization problem
We are given the problem min x3-x42 such that (1): x12 + x3 = 2 and (2): (x2-x4)(x2+x4)=1.

What I did was solve for x3 in (1) and then solve for x4 in (2). I substituted those equations into min x3-x42 and I obtain the solution: 2-x12-x22+1, would this be the correct approach to this problem?

Thank you!
 
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  • #2
You wrote ##x_{4^2}## in a couple of places, is that supposed to be ##x_4^2##?
I think you solved the problem just fine, but I would note literally solving for. ##x_4## requires writing down a ##\pm## which you have to carefully observe goes away when you square it, whereas solving for ##x_4^2## and substituting that does not.

Are you supposed to solve this? It's kind of weird, I think maybe they wanted you to maximize the function?
 
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  • #3
Office_Shredder said:
You wrote ##x_{4^2}## in a couple of places, is that supposed to be ##x_4^2##?
I think you solved the problem just fine, but I would note literally solving for. ##x_4## requires writing down a ##\pm## which you have to carefully observe goes away when you square it, whereas solving for ##x_4^2## and substituting that does not.

Are you supposed to solve this? It's kind of weird, I think maybe they wanted you to maximize the function?
Yes I meant it to be the second one my apologies. No no, I just needed to reduce it and was making sure I was on the right track, we do not have to maximize it, the next part is to do DFP on the problem.
 
  • #4
@ver_mathstats, please don't write acronyms without explaining what they mean. For example, I guess that NLO stands for nonlinear optimization, but I have no idea what DFP stands for.
 
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  • #5
Mark44 said:
@ver_mathstats, please don't write acronyms without explaining what they mean. For example, I guess that NLO stands for nonlinear optimization, but I have no idea what DFP stands for.
Sorry I'm just so used to that, it's the David Fletcher Powell method.
 
  • #6
ver_mathstats said:
Sorry I'm just so used to that, it's the David Fletcher Powell method.
I'd be surprised if anyone at this site has heard of this guy, let alone would know what DFP stands for.
 

FAQ: Given an NLO reduce it to unconstrained optimization problem

1. What is an NLO?

An NLO, or nonlinear optimization problem, is a mathematical problem that involves finding the optimal value of a function when the function is not linear. This means that the function cannot be solved using simple algebraic methods and requires more advanced techniques.

2. How is an NLO different from a linear optimization problem?

An NLO is different from a linear optimization problem because the objective function and/or constraints are not linear. This means that the shape of the function is more complex and cannot be solved using simple methods like gradient descent or linear programming.

3. What does it mean to reduce an NLO to an unconstrained optimization problem?

Reducing an NLO to an unconstrained optimization problem means finding a way to transform the original problem into a simpler problem with no constraints. This can make the problem easier to solve and can also provide insights into the original problem.

4. What are some techniques for reducing an NLO to an unconstrained optimization problem?

There are several techniques for reducing an NLO to an unconstrained optimization problem, including substitution, linearization, and transformation. These techniques involve manipulating the original problem to eliminate constraints and simplify the objective function.

5. Why would someone want to reduce an NLO to an unconstrained optimization problem?

Reducing an NLO to an unconstrained optimization problem can make the problem easier to solve and can also provide insights into the original problem. It can also allow for the use of simpler optimization techniques, such as gradient descent, which are not applicable to NLOs.

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