Optimization problem: minimization

In summary, the problem asks to minimize the function f(x,y) = \sqrt{x^2 + y^2} subject to x + y \leq 0. The solution is trivial, at (0,0), and can be proven using the Karush-Kuhn-Tucker conditions. The function MP(z) is not differentiable at z = 0, and is defined as the infimum of the set of all values of f(x,y) satisfying x + y \leq z. The domain of MP(z) is the set of all z such that there are points (x,y) satisfying x + y \leq z.
  • #1
rayge
25
0

Homework Statement


Minimize the function [itex]f(x,y) = \sqrt{x^2 + y^2}[/itex] subject to [itex]x + y \leq 0[/itex]. Show that the function [itex]MP(z)[/itex] is not differentiable at [itex]z = 0[/itex].

Homework Equations

The Attempt at a Solution


I haven't gotten anywhere because I don't understand why the solution isn't trivial, i.e. (0,0). Any suggestions as to where to start are welcome. Thanks!
 
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  • #2
Your trivial solution gives f(x,y)=0. Are there any others? If there are, then they are all minimizers. If not, then I think you are done.
What about the differentiable piece? What is meant by MP(z)?
 
  • #3
rayge said:

Homework Statement


Minimize the function [itex]f(x,y) = \sqrt{x^2 + y^2}[/itex] subject to [itex]x + y \leq 0[/itex]. Show that the function [itex]MP(z)[/itex] is not differentiable at [itex]z = 0[/itex].

Homework Equations

The Attempt at a Solution


I haven't gotten anywhere because I don't understand why the solution isn't trivial, i.e. (0,0). Any suggestions as to where to start are welcome. Thanks!
Your constraint is the line y = -x together with the half-plane below it.

What is MP(z)? That information should be in the problem statement.
 
  • #4
sorry about that. MP(z) is the infimum of the set of all values of [itex]f(x,y)[/itex] satisfying [itex]x + y \leq z[/itex], i.e. the minimum value of f(x,y) if it exists, or the greatest lower bound of it. The domain of MP(z) is the set of all z such that there are points (x,y) satisfying [itex]x + y \leq z[/itex].

Formally:
[itex]MP(z) = inf\{f(x) | x \epsilon C, g(x) \leq z\}[/itex]
where C is a convex set, in this case R^2

I haven't worked on showing the function isn't differentiable at z=0 yet, I'll probably have questions about that too. I'm mostly confused about why there is anything to solve in the first part of the problem.
 
  • #5
The solution to the first part is straightforward. Assume there is a minimum that is not (0,0) and contradict your assumption by showing that f(x,y)>0=f(0,0).
 
  • #6
rayge said:

Homework Statement


Minimize the function [itex]f(x,y) = \sqrt{x^2 + y^2}[/itex] subject to [itex]x + y \leq 0[/itex]. Show that the function [itex]MP(z)[/itex] is not differentiable at [itex]z = 0[/itex].

Homework Equations

The Attempt at a Solution


I haven't gotten anywhere because I don't understand why the solution isn't trivial, i.e. (0,0). Any suggestions as to where to start are welcome. Thanks!
The optimal solution IS trivial---you can see that geometrically--- and it is at (0,0), as you have stated.

If you have studied the Karush-Kuhn-Tucker conditions, you will see that (0,0) satisfies the necessary conditions for a minimum, but in the equivalent convex problem ##\min \; x^2 + y^2 \;\; \text{subject to } \; x + y \leq 0##. Since this is a convex programming problem, any local minimum is a global minimum, so the origin is provably the only solution---again, though, that is about as obvious as you can get.
 

Related to Optimization problem: minimization

1. What is an optimization problem?

An optimization problem is a mathematical problem that involves finding the best possible solution from a set of possible solutions. The goal is to minimize or maximize a certain objective function while satisfying a set of constraints.

2. What is minimization in an optimization problem?

Minimization in an optimization problem refers to the process of finding the smallest possible value for the objective function while satisfying the given constraints. This is typically done by adjusting the values of the variables in the problem.

3. How do you solve an optimization problem?

There are various methods for solving optimization problems, such as calculus, linear algebra, and algorithms like gradient descent. The specific approach depends on the type of problem and the available resources.

4. What are some real-world examples of minimization problems?

Minimization problems arise in many fields, including engineering, economics, and physics. Some common examples include minimizing production costs, minimizing energy consumption, and minimizing travel time in transportation systems.

5. How important is minimizing in optimization problems?

Minimizing is a crucial aspect of optimization problems as it helps us find the most efficient and effective solution. By minimizing the objective function, we can achieve the desired outcome while using the minimum amount of resources or effort.

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