Lin. Alg. - Half Planes (convex)

In summary, the author is looking for a way to prove that a half plane is convex. They are having trouble figuring out what to let P and Q be equal to in order to show that the half plane is convex. They think that maybe using the dot product and scalar multiplication will work. They use the example of A = (a1, a2, ..., an), P = (x1, x2,..., xn) and Q = (y1, y2,..., yn) where P and Q are points in X. They show that A \cdot P \geq c and A \cdot Q \geq c. Then they use the distributive law and the
  • #1
mattmns
1,128
6
Hello, I am a little confused on how to prove these half planes are convex, and my book does not actually show an example.
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So here is a problem, my book briefly talks about it: Show that the plane 2x - 3y >= 6 is convex. So if we let A = (2,-3), and X = (x,y), then we can write the inequality [itex]A \cdot X >= 6[/itex]. Prove that this half plane is convex.
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So I want to let there be a point P in this half plane, and a point Q on this plane, and then show that (1-t)P + tQ is of the same form and therefore the half plane is convex. The problem I am having is what do I let P and Q be equal to, in order to show this? Thanks.
 
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  • #2
P and Q must be points in the half-plane, of course. That is P= (x,y) where 2x- 3y>= 6, Q= (u, v) where 2u- 3v>= 6. As you say, the line segment between them is (1-t)P+ tQ= ((1-t)x+ tu, (1-t)y+ tv). Now can you show that, as long as 0<= t<= 1, that point is also in the half plane? That is, that 2[(1-t)x+ tu]- 3[(1-t)y+ tv]>= 6.
 
  • #3
Thanks. The real problem is now to prove it in Rn :smile:

To make more clear, here is the actual problem from the book: Let A be a non-zero vector in Rn and let c be a fixed number. Show that the set of all elements X in Rn such that [itex]A \cdot X >= c[/itex] is convex.So I would just let A = (a1, a2, ..., an), P = (x1, x2,..., xn) and Q = (y1, y2,..., yn) where P and Q are points in X, so [itex]A \cdot P >= c[/itex] and [itex]A \cdot Q >= c[/itex]. And then (1-t)P+ tQ = ( (1-t)x1 + ty1, (1-t)x2 + ty2, ... , (1-t)xn + tyn) and show that this point is in X for 0 <= t <= 1. Meaning, [itex]a_{1}[(1-t)x_{1} + ty_{1}] + a_{2}[(1-t)x_{2} + ty_{2}], + ... + a_{n}[(1-t)x_{n} + ty_{n}] >= c[/itex]

I have not worked this out yet, but I think it should work.
 
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  • #4
Yep. Separate the xs from the ys and see what you get.
 
  • #5
Ok, I just worked on the problem, and I think I have found a somewhat shorter way to do it. Well maybe it is not shorter, but instead I don't write everything out. I use the idea that the dot product is distributive and scalar multiplication is allowed (1-t), t.

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let A = (a1, a2, ..., an), P = (x1, x2,..., xn) and Q = (y1, y2,..., yn) where P and Q are points in X, so [itex]A \cdot P \geq c[/itex] and [itex]A \cdot Q \geq c[/itex]. So, we must show that [itex]A \cdot (1-t)P+ tQ \geq c[/itex]

but [itex]A \cdot [(1-t)P+ tQ] \ = \ A \cdot P(1-t) + A \cdot Qt[/itex]
and [itex]A \cdot P(1-t) + A \cdot Qt \geq c[/itex] is clearly true because [itex]A \cdot P \geq c[/itex], and [itex]A \cdot Q \geq c[/itex]
and so if we factor we have ((1-t) + t)(something greater than c) = 1(something greater than c).
So, [itex]A \cdot [(1-t)P+ tQ] \geq c[/itex]

=> (1-t)P + tQ is in X, and therefore X is convex.
------Does that look sufficient?

Also, the end looks a bit nasty, the whole "(something greater than c)" part, is there a prettier way to write this? Thanks!
 
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FAQ: Lin. Alg. - Half Planes (convex)

What is a convex half plane in linear algebra?

A convex half plane is a geometric concept that refers to a region of space that is bounded by a line and contains all points on one side of that line. In linear algebra, a convex half plane is often used to describe the feasible region of a linear inequality.

How is a convex half plane represented mathematically?

In linear algebra, a convex half plane can be represented by a linear inequality in the form of ax + by ≤ c, where a and b are coefficients of the variables x and y, and c is a constant. The line ax + by = c is the boundary of the half plane, and the side of the line that satisfies the inequality is the convex half plane.

What is the significance of convex half planes in linear programming?

Convex half planes are essential in linear programming because they help define the feasible region, which is the set of all possible solutions to a linear programming problem. The feasible region is bounded by convex half planes, and the optimal solution of the problem will always lie within this region.

How are convex half planes related to convex sets?

In linear algebra, a convex half plane is a specific type of convex set, which is a set where any two points can be connected by a straight line that stays entirely within the set. Convex half planes are also considered convex sets because any two points within the half plane can be connected by a straight line that lies entirely within the half plane.

Can convex half planes be used in higher dimensions?

Yes, convex half planes can be extended to higher dimensions in linear algebra. In three-dimensional space, a convex half plane is defined by a linear inequality in the form of ax + by + cz ≤ d, where a, b, and c are coefficients of the variables x, y, and z, and d is a constant. The concept can be extended to even higher dimensions in a similar manner.

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