Feasible Directions & FONC

  • Thread starter Nugso
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In summary, the point satisfies FONC if and only if there exists a feasible direction that has both ##d_1## and ##d_2## as nonnegative numbers.
  • #1
Nugso
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Homework Statement



[tex] \text{Minimize } f(x) [/tex]
[tex] \text{Subject to } \Omega [/tex]

where [tex] f:R^2 → R \text{ is given by } f(x) = -3x_1 \text{ where } x = [2,0]^\top \text{ and } \Omega = \{x: x_1 + 2x_2^2 \leq 2\}[/tex]

[tex] \text{Does the point } x^* = [2,0]^\top \text{satisfy F.O.N.C?} [/tex]

Homework Equations



[tex] d^T\nabla{f(x^*)} \geq 0 [/tex]

has to be satisfied for FONC.

The Attempt at a Solution



I know FONC, but what I'm having trouble understanding is determining feasible directions. For example, in this case, the point is on the boundary.

[tex] x + ad = [2,0]^\top + a [d_1, d_2]^\top[/tex]

Now, applying the constraint, I have

[tex] 2ad_1 + 2(ad_2)^2 \leq 0[/tex]

From there, it seems like d_2 is an arbitrary choice, while d_1 has to be chosen accordingly. But, also, since the point is on the corner of the boundary, doesn't that mean that d_2 has to be in some specific direction as well?
 
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  • #2
Nugso said:

Homework Statement



[tex] \text{Minimize } f(x) [/tex]
[tex] \text{Subject to } \Omega [/tex]

where [tex] f:R^2 → R \text{ is given by } f(x) = -3x_1 \text{ where } x = [2,0]^\top \text{ and } \Omega = \{x: x_1 + 2x_2^2 \leq 2\}[/tex]

[tex] \text{Does the point } x^* = [2,0]^\top \text{satisfy F.O.N.C?} [/tex]

Homework Equations



[tex] d^T\nabla{f(x^*)} \geq 0 [/tex]

has to be satisfied for FONC.

The Attempt at a Solution



I know FONC, but what I'm having trouble understanding is determining feasible directions. For example, in this case, the point is on the boundary.

[tex] x + ad = [2,0]^\top + a [d_1, d_2]^\top[/tex]

Now, applying the constraint, I have

[tex] 2ad_1 + 2(ad_2)^2 \leq 0[/tex]

From there, it seems like d_2 is an arbitrary choice, while d_1 has to be chosen accordingly. But, also, since the point is on the corner of the boundary, doesn't that mean that d_2 has to be in some specific direction as well?

What does the term "FONC" mean? I get the "FO" = "first-order" part, but what is NC?
 
  • #3
Ray Vickson said:
What does the term "FONC" mean? I get the "FO" = "first-order" part, but what is NC?
Hi Ray. It's First Order Necessary Condition.
 
  • #4
Nugso said:

Homework Statement



[tex] \text{Minimize } f(x) [/tex]
[tex] \text{Subject to } \Omega [/tex]

where [tex] f:R^2 → R \text{ is given by } f(x) = -3x_1 \text{ where } x = [2,0]^\top \text{ and } \Omega = \{x: x_1 + 2x_2^2 \leq 2\}[/tex]

[tex] \text{Does the point } x^* = [2,0]^\top \text{satisfy F.O.N.C?} [/tex]

Homework Equations



[tex] d^T\nabla{f(x^*)} \geq 0 [/tex]

has to be satisfied for FONC.

The Attempt at a Solution



I know FONC, but what I'm having trouble understanding is determining feasible directions. For example, in this case, the point is on the boundary.

[tex] x + ad = [2,0]^\top + a [d_1, d_2]^\top[/tex]

Now, applying the constraint, I have

[tex] 2ad_1 + 2(ad_2)^2 \leq 0[/tex]

From there, it seems like d_2 is an arbitrary choice, while d_1 has to be chosen accordingly. But, also, since the point is on the corner of the boundary, doesn't that mean that d_2 has to be in some specific direction as well?

I suggest you draw some feasible directions ##\vec{d} = [d_1,d_2]##. Remember that a point of the form ##\vec{x} = [2,0] + t \vec{d}## only need to satisfy the constraint for small, positive ##t##. In particular, if ##d_1=0## then also you need ##d_2 = 0##. Also, of course, you cannot have ##d_1 > 0##; why not?
 
  • #5
Ray Vickson said:
I suggest you draw some feasible directions ##\vec{d} = [d_1,d_2]##. Remember that a point of the form ##\vec{x} = [2,0] + t \vec{d}## only need to satisfy the constraint for small, positive ##t##. In particular, if ##d_1=0## then also you need ##d_2 = 0##. Also, of course, you cannot have ##d_1 > 0##; why not?

When I imagine (draw), a coordinate system, with x and y, with our point on the corner of the boundary, I'm inclined to say both ##d_1## and ##d_2## needs to be nonnegative, because otherwise ##d## wouldn't be a feasible direction.

However, as I've said, if I apply ##d## to the constraint inequality, I only get ##d_1 \geq0##

For example, if I had the same constraints, but this time our point was only on the boundary (not necessarily corner), I could see why it'd be only ##d_1 \geq0##. But I'm having trouble understanding this.
 
  • #6
Nugso said:
When I imagine (draw), a coordinate system, with x and y, with our point on the corner of the boundary, I'm inclined to say both ##d_1## and ##d_2## needs to be nonnegative, because otherwise ##d## wouldn't be a feasible direction.

However, as I've said, if I apply ##d## to the constraint inequality, I only get ##d_1 \geq0##

For example, if I had the same constraints, but this time our point was only on the boundary (not necessarily corner), I could see why it'd be only ##d_1 \geq0##. But I'm having trouble understanding this.

By definition, ##\vec{d} = [d_1,d_2]## is a feasible direction at ##\vec{x}_0## if ##\vec{x} = \vec{x}_0 + t \vec{d}## is feasible for all small enough ##t > 0## (note the sign!). So, for ##\vec{x}_0 = [2,0]##, is ##\vec{d} = [1,0]## feasible? Is ##\vec{d} = [1,1]## feasible? Is ##\vec{d} = [1, -0.01]## feasible? Is ##\vec{d} = [-1,10]## feasible? What about ##\vec{d} = [-1, -1/2]?##
 
  • #7
Ray Vickson said:
By definition, ##\vec{d} = [d_1,d_2]## is a feasible direction at ##\vec{x}_0## if ##\vec{x} = \vec{x}_0 + t \vec{d}## is feasible for all small enough ##t > 0## (note the sign!). So, for ##\vec{x}_0 = [2,0]##, is ##\vec{d} = [1,0]## feasible? Is ##\vec{d} = [1,1]## feasible? Is ##\vec{d} = [1, -0.01]## feasible? Is ##\vec{d} = [-1,10]## feasible? What about ##\vec{d} = [-1, -1/2]?##

Okay, so, if I were to use the definition, I'd say only ## d = [-1, 10] ## and ## d = [-1, -1/2 ] ## are feasible. However, when I'm trying to picture it, I get the following:

yu4UwPH.png


with the blue curve and line [2,-2] the set red lines inside of the set, I think the feasible directions have to be inside the set because we're right at the corner. So by that logic

## d = [1,0] ## and ## d = [1,1] ##are also feasible. I think either I'm drawing something incorrect, or I just simply can't interpret it.
 
  • #8
Nugso said:
Okay, so, if I were to use the definition, I'd say only ## d = [-1, 10] ## and ## d = [-1, -1/2 ] ## are feasible. However, when I'm trying to picture it, I get the following:

yu4UwPH.png


with the blue curve and line [2,-2] the set red lines inside of the set, I think the feasible directions have to be inside the set because we're right at the corner. So by that logic

## d = [1,0] ## and ## d = [1,1] ##are also feasible. I think either I'm drawing something incorrect, or I just simply can't interpret it.

If you go any positive distance at all along ##\vec{d} = [1,0]## you go into the infeasible region (the unshaded region in your drawing)! Same for ##\vec{d} = [1,1]##.
 
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  • #9
Ray Vickson said:
If you go any positive distance at all along ##\vec{d} = [1,0]## you go into the infeasible region (the unshaded region in your drawing)! Same for ##\vec{d} = [1,1]##.

Ohh! I think I got it now! Thanks!
 

What is a feasible direction?

A feasible direction is a direction in which all constraints of a mathematical optimization problem are satisfied. This means that the solution in this direction does not violate any of the constraints.

What is the significance of feasible directions in optimization?

Feasible directions play a crucial role in optimization as they help determine the direction in which the objective function can be improved without violating any constraints. They are used in algorithms such as the method of feasible directions to find the optimal solution.

What is the FONC?

FONC stands for First-Order Necessary Condition, which is a mathematical condition that must be satisfied by any point that is a local minimum or maximum of a function. It states that the derivative of the function at that point must be equal to zero.

How is the FONC related to feasible directions?

The FONC is related to feasible directions in optimization as it is used to determine whether a given direction is feasible or not. If the derivative of the objective function in a particular direction is positive, then that direction is considered feasible, and the solution can be improved in that direction.

What is the method of feasible directions?

The method of feasible directions is an algorithm used to solve constrained optimization problems. It involves finding a feasible direction in which the objective function can be improved, and then repeating the process until the optimal solution is reached.

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