Maximum principle - don't understand why RHS is negative.

In summary, the conversation discusses a linear inhomogeneous second order two point BVP and a question regarding the maximum value of u(x). The first question states that if f(x) is negative on the interval [0,1], then u(x) will attain its maximum value at one of the endpoints. The second question involves substituting v(x) into the equation and proving that u(x) will still attain its maximum value at one of the endpoints if f(x) is less than or equal to 0. The solution to the second question involves analyzing the signs of the terms within the brackets and showing that for sufficiently large λ, the quadratic term will dominate and make the whole expression negative.
  • #1
Silversonic
130
1

Homework Statement



Consider the linear inhomogeneous second order two point BVP;

-a(x)u''(x) + b(x)u'(x) = f(x) for 0 < x < 1

for some functions a, f, b where a(x) > 0 for all x

1) If f(x) < 0 for x = [0,1], show that u(x) attains its maximum value at one of the two end points x = 0, 1. - I've done this, anyone who has done the maximum principle should be able to as well.

2) Substitute v(x) = u(x) + [itex]\epsilon[/itex] [itex]e^{\lambda x}[/itex], show that if f(x) [itex]\leq[/itex] 0 then u(x) attains its maximum value at one of the end points x = 0,1

The Attempt at a Solution



Question two is what I'm stuck on. If we substitute v(x) into the equation instead we get

[PLAIN]http://img819.imageshack.us/img819/9646/unledmug.jpg

It says the RHS is strictly negative since a(x) > 0. How can we know this, when we don't know what sign b(x) may come out as for any x? It may turn out b(x) is positive and [itex]\lambda b[/itex] is greater than |[itex]\lambda^{2 } a[/itex]| and therefore what is contained within the brackets is positive, and may be bigger than the absolute value of f(x). Making the whole thing positive.

Am I missing something?
 
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  • #2
for any x, b(x)λ is a linear function in λ.

but a(x)λ2 is quadratic, so for sufficiently large λ, this term will dominate.
 

FAQ: Maximum principle - don't understand why RHS is negative.

What is the maximum principle?

The maximum principle is a fundamental concept in mathematics that states that the maximum value of a function over a closed and bounded region will be found either at the boundary or at a critical point within the region.

How does the maximum principle apply to the RHS being negative?

The maximum principle applies to the RHS (right-hand side) being negative because it dictates that the maximum value of a function over a region will be at the boundary or a critical point. If the RHS is negative, then the maximum value of the function will not be found within the region, as it would result in a contradiction.

Why is the RHS typically negative in the maximum principle?

The RHS is typically negative in the maximum principle because it represents the negative influence of the boundary conditions on the function being maximized. This negative influence is necessary for the maximum principle to hold true.

Can you provide an example of the maximum principle in action?

One example of the maximum principle is in the heat equation, where the temperature at a point within a region will be at its maximum at the boundary of the region or at a critical point within the region. This is because the boundary conditions, such as the temperature of the surrounding environment, affect the temperature at the point being considered.

How is the maximum principle used in real-world applications?

The maximum principle is used in various fields, including physics, engineering, and economics, to analyze and optimize systems. For example, in economics, the maximum principle can be used to determine the optimal production level for a company, taking into account external factors such as market demand and production costs.

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