Does the Point [2,0] Satisfy the First Order Necessary Conditions?

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Homework Help Overview

The discussion revolves around determining whether the point [2,0] satisfies the First Order Necessary Conditions (FONC) for a minimization problem subject to a constraint. The function to minimize is f(x) = -3x1, and the constraint is defined by the region Ω = {x: x1 + 2x2^2 ≤ 2}. Participants are exploring the implications of being at the boundary of the feasible region.

Discussion Character

  • Exploratory, Assumption checking, Problem interpretation

Approaches and Questions Raised

  • Participants discuss the concept of feasible directions at the boundary point and how to apply the constraint to determine these directions. There is confusion regarding the implications of being at the corner of the boundary and how it affects the choice of d1 and d2. Questions arise about the meaning of FONC and the feasibility of various directional choices.

Discussion Status

The discussion is active, with participants sharing their understanding of feasible directions and the constraints involved. Some guidance has been offered regarding the definition of feasible directions and the implications of moving from the corner point. There is an ongoing exploration of different interpretations of the constraints and their effects on the problem.

Contextual Notes

Participants are grappling with the specifics of the boundary conditions and the nature of feasible directions in relation to the given constraint. The discussion highlights the complexity of interpreting the conditions at a corner point in the feasible region.

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Homework Statement



\text{Minimize } f(x)
\text{Subject to } \Omega

where 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\}

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

Homework Equations



d^T\nabla{f(x^*)} \geq 0

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.

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

Now, applying the constraint, I have

2ad_1 + 2(ad_2)^2 \leq 0

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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Nugso said:

Homework Statement



\text{Minimize } f(x)
\text{Subject to } \Omega

where 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\}

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

Homework Equations



d^T\nabla{f(x^*)} \geq 0

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.

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

Now, applying the constraint, I have

2ad_1 + 2(ad_2)^2 \leq 0

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?
 
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.
 
Nugso said:

Homework Statement



\text{Minimize } f(x)
\text{Subject to } \Omega

where 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\}

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

Homework Equations



d^T\nabla{f(x^*)} \geq 0

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.

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

Now, applying the constraint, I have

2ad_1 + 2(ad_2)^2 \leq 0

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?
 
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.
 
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]?##
 
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.
 
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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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!
 

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