I'm asking this as an absolute beginner (second year undergrad student). Suppose I write an equation out of the blue such as $$\log(\tan(\sin x + 23e^{2x+83} +\tan x))= 1000$$, which is non-linear, how would a mathematician attempt to solve it? I am encountering subjects which get more and more abstract(linear algebra, group theory) and I wonder if they are applicable to such problems.
How do mathematicians work with non-linear equations
linear algebratrigonometry
Related Solutions
Friedberg is not wrong, at least on a historical standpoint, as I am going to try to show it.
Determinants were discovered "as such" in the second half of the 18th century by Cramer who used them in his celebrated rule for the solution of a linear system (in terms of quotients of determinants). Their spread was rather rapid among mathematicians of the next two generations ; they discovered properties of determinants that now, with our vision, we mostly express in terms of matrices.
Cauchy has given two important results about determinants as explained in the very nice article by Hawkins referenced below :
around 1815, Cauchy discovered the multiplication rule (rows times columns) of two determinants. This is typical of a result that has been completely revamped : nowadays, this rule is for the multiplication of matrices, and determinants' multiplication is restated as the homomorphism rule $\det(A \times B)= \det(A)\det(B)$.
around 1825, he discovered eigenvalues "associated with a symmetric determinant" and established the important result that these eigenvalues are real ; this discovery has its roots in astronomy, in connection with Sturm, explaining the word "secular values" he attached to them: see for example this.
Matrices made a shy apparition in the mid-19th century (in England) ; "matrix" is a term coined by Sylvester see here. I strongly advise to take a look at his elegant style in his Collected Papers.
Together with his friend Cayley, they can rightly be named the founding fathers of linear algebra, with determinants as permanent reference. Here is a major quote of Sylvester:
"I have in previous papers defined a "Matrix" as a rectangular array of terms, out of which different systems of determinants may be engendered as from the womb of a common parent".
A lot of important polynomials are either generated or advantageously expressed as determinants:
the characteristic polynomial (of a matrix) is expressed as the famous $\det(A-\lambda I)$,
in particular, the theory of orthogonal polynomials mainly developed at the end of 19th century, can be expressed in great part with determinants,
the "resultant" of two polynomials, invented by Sylvester (giving a condition for these polynomials to have a common root), etc.
Let us repeat it : for a mid-19th century mathematician, a square array of numbers has necessarily a value (its determinant): it cannot have any other meaning. If it is a rectangular array, the numbers attached to it are the determinants of submatrices that can be "extracted" from the array.
The identification of "Linear Algebra" as an integral (and new) part of Mathematics is mainly due to the German School (say from 1870 till the 1930's). I don't cite the names, there are too many of them. An example among many others of this german domination: the germenglish word "eigenvalue". The word "kernel" could have remained the german word "kern" that appears around 1900 (see this site).
The triumph of Linear Algebra is rather recent (mid-20th century). "Triumph" meaning that now Linear Algebra has found a very central place. Determinants in all that ? Maybe the biggest blade in this swissknife, but not more ; another invariant (this term would deserve a long paragraph by itself), the trace, would be another blade, not the smallest.
In 19th century, Geometry was still at the heart of mathematical education; therefore, the connection between geometry and determinants has been essential in the development of linear algebra. Some cornerstones:
- the development of projective geometry, in its analytical form, in the 1850s. This development has led in particular to place homographies at the heart of projective geometry, with their associated matricial expression. Besides, conic curves, described by a quadratic form, can as well be written under an all-matricial expression $X^TMX=0$ where $M$ is a symmetrical $3 \times 3$ matrix. This convergence to a unique and new "algebra" has taken time to be recognized.
A side remark: this kind of reflexions has been capital in the decision of Bourbaki team to avoid all figures and adopt the extreme view of reducing geometry to linear algebra (see the "Down with Euclid" of J. Dieudonné in the sixties).
Different examples of the emergence of new trends :
a) the concept of rank: for example, a pair of straight lines is a conic section whose matrix has rank 1. The "rank" of a matrix used to be defined in an indirect way as the "dimension of the largest nonzero determinant that can be extracted from the matrix". Nowadays, the rank is defined in a more straightforward way as the dimension of the range space... at the cost of a little more abstraction.
b) the concept of linear transformations and duality arising from geometry: $X=(x,y,t)^T\rightarrow U=MX=(u,v,w)$ between points $(x,y)$ and straight lines with equations $ux+vy+w=0$. More precisely, the tangential description, i.e., the constraint on the coefficients $U^T=(u,v,w)$ of the tangent lines to the conical curve has been recognized as associated with $M^{-1}$ (assuming $\det(M) \neq 0$!), due to relationship
$$X^TMX=X^TMM^{-1}MX=(MX)^T(M^{-1})(MX)=U^TM^{-1}U=0$$ $$=\begin{pmatrix}u&v&w\end{pmatrix}\begin{pmatrix}A & B & D \\ B & C & E \\ D & E & F \end{pmatrix}\begin{pmatrix}u \\ v \\ w \end{pmatrix}=0$$
whereas, in 19th century, it was usual to write the previous quadratic form as :
$$\det \begin{pmatrix}M^{-1}&U\\U^T&0\end{pmatrix}=\begin{vmatrix}a&b&d&u\\b&c&e&v\\d&e&f&w\\u&v&w&0\end{vmatrix}=0$$
as the determinant of a matrix obtained by "bordering" $M^{-1}$ precisely by $U$
(see the excellent lecture notes (http://www.maths.gla.ac.uk/wws/cabripages/conics/conics0.html)). It is to be said that the idea of linear transformations, especially orthogonal transformations, arose even earlier in the framework of the theory of numbers (quadratic representations).
Remark: the way the former identities have been written use matrix algebra notations and rules that were unknown in the 19th century, with the notable exception of Grassmann's "Ausdehnungslehre", whose ideas were too ahead of his time (1844) to have a real influence.
c) the concept of eigenvector/eigenvalue, initially motivated by the determination of "principal axes" of conics and quadrics.
- the very idea of "geometric transformation" (more or less born with Klein circa 1870) associated with an array of numbers (when linear or projective). A matrix, of course, is much more that an array of numbers... But think for example to the persistence of expression "table of direction cosines" (instead of "orthogonal matrix") as can be found for example still in the 2002 edition of Analytical Mechanics by A.I. Lorrie.
d) The concept of "companion matrix" of a polynomial $P$, that could be considered as a tool but is more fundamental than that (https://en.wikipedia.org/wiki/Companion_matrix). It can be presented and "justified" as a "nice determinant" : In fact, it has much more to say, with the natural interpretation for example in the framework of $\mathbb{F}_p[X]$ (polynomials with coefficients in a finite field) as the matrix of multiplication by $P(X)$. (https://glassnotes.github.io/OliviaDiMatteo_FiniteFieldsPrimer.pdf), giving rise to matrix representations of such fields. Another remarkable application of companion matrices : the main numerical method for obtaining the roots of a polynomial is by computing the eigenvalues of its companion matrix using a Francis "QR" iteration (see (https://math.stackexchange.com/q/68433)).
References:
I discovered recently a rather similar question with a very complete answer by Denis Serre, a specialist in the domain of matrices : https://mathoverflow.net/q/35988/88984
The article by Thomas Hawkins : "Cauchy and the spectral theory of matrices", Historia Mathematica 2, 1975, 1-29.
See also (http://www.mathunion.org/ICM/ICM1974.2/Main/icm1974.2.0561.0570.ocr.pdf)
An important bibliography is to be found in (http://www-groups.dcs.st-and.ac.uk/history/HistTopics/References/Matrices_and_determinants.html).
See also a good paper by Nicholas Higham : (http://eprints.ma.man.ac.uk/954/01/cay_syl_07.pdf)
For conic sections and projective geometry, see a) this excellent chapter of lectures of the University of Vienna (see the other chapters as well) : (https://www-m10.ma.tum.de/foswiki/pub/Lehre/WS0809/GeometrieKalkueleWS0809/ch10.pdf). See as well : (maths.gla.ac.uk/wws/cabripages/conics/conics0.html).
Don't miss the following very interesting paper about various kinds of useful determinants : https://arxiv.org/pdf/math/9902004.pdf
See also this
Very interesting precisions on determinants in this text and in these answers.
A fundamental work on "The Theory of Determinants" in 4 volumes has been written by Thomas Muir : http://igm.univ-mlv.fr/~al/Classiques/Muir/History_5/VOLUME5_TEXT.PDF (years 1906, 1911, 1922, 1923) for the last volumes or, for all of them https://ia800201.us.archive.org/17/items/theoryofdetermin01muiruoft/theoryofdetermin01muiruoft.pdf. It is very interesting to take random pages and see how the determinant-mania has been important, especially in the second half of the 19th century. Matrices appear at some places with the double bar convention that lasted a very long time. Matrices are mentionned here and there, rarely to their advantage...
Many historical details about determinants and matrices can be found here.
Advanced Linear Algebra by Roman is a nice book: http://www.springer.com/gp/book/9780387728285.
If you're looking for connections between linear algebra and geometry, then I'd recommend just immediately studying differential geometry. A good place to start here is An Introduction to Manifolds by Tu: http://www.springer.com/gp/book/9781441973993.
The study of infinite dimensional vector spaces is called functional analysis and a good place to start here is Introductory Functional Analysis with Applications by Kreyszig: http://au.wiley.com/WileyCDA/WileyTitle/productCd-0471504599.html.
Best Answer
The main way to solve non-linear equations is using the computer. There are lots of tools that can be used if you do not need a formula for the solution.
The computer will give you some numbers $x$ that you can put into the formula you want to solve, and you will see that the numbers solves the equation, such that the left hand side equals 1000.000000 within numerical precision.
If you want a formula, the situation is different. You might use a corollary of the inverse function theorem to prove that there exists a formula that solves the equation exactly. But of course, even if it exists, it might not be practical to write it down on any sized-peace of paper because it is so large or our tools for understanding the contents is too weak. Please add comments if you want to know more.