# How and why vector spaces are defined?

Is it true that vector spaces are defined to check if system of linear equations is solvable or not?

Explanation: Goal is to solve system of linear equations.

In matrix form: $Ax = b$. As $A = [C_1 \; C_2 \; ... \; C_n]$, where $C_n$ is a column and $x = [x_1 \; x_2 \; ... \; x_n]$.

Therefore, $C_1x_1 + C_2x_2 + ....+ C_nx_n = b$. Linear combination of column vectors produce vector $b$.

Because of above statement (linear combination) we choose a set of vectors that have closure under addition and scalar multiplication (closure under linear combination) and call that set of vectors a vector space. Now, if vector $b$ lies in that set of vectors (vector space) then only system of linear equations is solvable.

• Vector spaces are a fairly natural mathematical structure that are interesting enough by themselves to be defined purely for study. They can be used to solve a system of linear equations, but they are also good for a lot more than just that. Jul 9 '18 at 8:49
• If one is to consider your question literally, the answer is an unqualified NO: there are plenty of other applications of the concept. Perhaps you mean your question in the historical context. Then you should reformulate it, and tag it math-history. Jul 9 '18 at 8:51
• Incidentally, I think that downvoting the question, without any explanation, is much too harsh. Jul 9 '18 at 8:54
• I don't know but it might be true that, historically, the set of solutions to a homogeneous linear differential equation provided one of the best early examples of a vector space that is not $\mathbb R^n$, and thus helped lead mathematicians to discover the notion of a vector space. Jul 9 '18 at 10:36
• @prokilogrammer Please recall that if the OP is solved you can evaluate to accept an answer among the given, more details here meta.stackexchange.com/questions/5234/…
– user
Aug 6 '18 at 21:54

The resolution of the systems of linear equations is one of the main goal but of course there are many others applications (i.e. least squares, dynamical systems, image compression, etc,.).

Please note that the notion of vector spaces and more generally that of $R$- Module is fundamental in maths about the last 200 years.
Vector spaces are the appropriate context for studying linear transformations. A transformation $T$ is linear if its maps a linear combination of two elements from its domain to a linear combination of the individual images of the two elements.
More formally $T$ is linear if $T(p \text{a} + q\text{b})) = pT(\text{a} + qT(\text{b})$. Less formally, we can say that $T$ is linear if its preserves straight lines i.e. the image of a straight line in its domoan is always a straight line in its range.