Linearity vs. takes straight lines to straight lines

In summary, the conversation discusses the proof of a theorem that states if a bijective map ##\Lambda## takes straight lines to straight lines and has the property ##\Lambda(0)=0##, then ##\Lambda## is linear. The conversation also mentions Fock's theorem, which provides a more general statement but is difficult to prove. The speaker is attempting to find a simpler proof of the theorem, and mentions an exercise in a book that may be a simpler version of the same theorem. The speaker also notes that some assumptions about smoothness may need to be included in the proof.
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Fredrik
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Linearity vs. "takes straight lines to straight lines"

Homework Statement



Prove that if ##\Lambda:\mathbb R^n\to\mathbb R^n## is a bijection that takes straight lines to straight lines, and is such that ##\Lambda(0)=0##, then ##\Lambda## is linear.

Homework Equations



Fock's theorem implies that the statement I want to prove is true. But I don't want to use that. I want to find a simpler proof of a less general theorem. It's also possible that there's a published proof of Fock's theorem that is easy to simplify then the assumptions are stronger.

Fock's theorem says (roughly) that if ##\Lambda## is a map that takes straight lines to straight lines, there's there's a 4×4 matrix A, two 4×1 matrices y,z, and a number c, such that
$$\Lambda(x)=\frac{Ax+y}{z^Tx+c}.$$ Note that if ##\Lambda## is defined on a vector space (rather than a proper subset of one), there's always an x that makes the denominator 0. So, since my ##\Lambda## has a vector space as its domain, we must have z=0, and we can redefine A and y to absorb c. But I'm also assuming that ##\Lambda(0)=0##, so the theorem also says that ##y=0##, which means that my ##\Lambda## is linear.

The proof of Fock's theorem is very hard. It looks like it's proved in Appendix B of this article, but the proof is so badly written that it's hard to tell. The proof is discussed in this thread. My thoughts on the first part of the proof appears in post #61.

The Attempt at a Solution



I'm nowhere near a solution. I guess the following is better than nothing, but only very slightly: Let x be arbitrary. Let v≠0 be arbitrary. Since ##\Lambda## takes straight lines to straight lines, there's a unit vector u and a function ##s:\mathbb R\to\mathbb R## such that for all t,
$$\Lambda(x+vt)=\Lambda(x)+s(t)u.$$ (The link to "post #61" above explains this in more detail). I guess I want to prove that ##t\Lambda(v)=s(t)u##, but I don't see how to proceed from here.

Also, my post #50 describes a few more of my thoughts about the problem.

One of the reasons why I think it that the simpler theorem has a simpler proof is that it's an exercise in a book I own. It's exercise 1.3.1(2) on page 9 of this book (pdf link). Unfortunately, the exercise is messed up in at least two ways. It assumes that the map is surjective onto W, but it doesn't define W. And it asks the reader to prove that the map is linear, but that can't be the correct conclusion since the exercise doesn't include the assumption that the map takes 0 to 0. The exercise also doesn't assume that the map is bijective, but including that assumption can only make things easier, right?

Edit: By the way, I'm not entirely clear on whether some assumptions about smoothness or at least existence of partial derivatives must be included.
 
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I got a tip that the "simple" theorem I want to prove is more or less "the fundamental theorem of affine geometry". It's quite hard to prove, so it really shouldn't be an exercise on page 9 of a book on functional analysis. :smile:
 

FAQ: Linearity vs. takes straight lines to straight lines

What is linearity?

Linearity refers to the property of a mathematical relationship between two variables where the output changes in a proportional manner to changes in the input. This means that when the input is multiplied by a certain factor, the output is also multiplied by the same factor.

What is the difference between linearity and non-linearity?

The main difference between linearity and non-linearity is that in a linear relationship, the output changes in a proportional manner to changes in the input, while in a non-linear relationship, the output changes in a non-proportional manner to changes in the input.

What are some real-world examples of linearity?

Some real-world examples of linearity include the relationship between distance and time for a constant speed, the relationship between force and displacement for a spring, and the relationship between concentration and absorbance for a solution in a spectrophotometer.

How is linearity important in scientific research?

Linearity is important in scientific research because it allows us to accurately predict the outcome of a mathematical relationship between variables. This is essential in fields such as physics, chemistry, and engineering where precise measurements and predictions are necessary.

What are some techniques used to test for linearity?

Some techniques used to test for linearity include plotting a scatter plot and visually inspecting for a linear pattern, performing a regression analysis and examining the R-squared value, and conducting a statistical test such as the F-test or the Durbin-Watson test.

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