Babylonian clay tablet YBC 7289
(c.
1800–1600 BCE)
[1] with annotations.
(Image by Bill Casselman)
Numerical analysis is the study of
algorithms for the problems of
continuous
mathematics (as distinguished from
discrete mathematics).
One of the earliest mathematical writings is the Babylonian tablet
YBC 7289, which gives a
sexagesimal
numerical approximation of \sqrt{2}, the length of the diagonal in
a unit square.The approximation of the
square root of 2 is four
sexagesimal figures, which is about six
decimal figures. 1 + 24/60 + 51/60
^{2} +
10/60
^{3} = 1.41421296...
Photograph, illustration, and description of the
root(2) tablet from the Yale Babylonian
CollectionBeing able to compute the sides of a triangle (and
hence, being able to compute square roots) is extremely important,
for instance, in carpentry and construction.
Numerical analysis continues this long tradition of practical
mathematical calculations. Much like the Babylonian approximation
to \sqrt{2}, modern numerical analysis does not seek exact answers,
because exact answers are often impossible to obtain in practice.
Instead, much of numerical analysis is concerned with obtaining
approximate solutions while maintaining reasonable bounds on
errors.
Numerical analysis naturally finds applications in all fields of
engineering and the physical sciences, but in the 21st century, the
life sciences and even the arts have adopted elements of scientific
computations.
Ordinary
differential equations appear in the
movement of heavenly bodies ;
optimization occurs in portfolio
management;
numerical linear
algebra is important for data analysis,
stochastic differential
equations and
Markov chains are
essential in simulating living cells for medicine and
biology.
Before the advent of modern computers numerical methods often
depended on hand
interpolation in
large printed tables. Since the mid 20th century, computers
calculate the required functions instead. The
interpolation algorithms nevertheless may be used as part of
the software for solving
differential equations.
General introduction
The overall goal of the field of numerical analysis is the design
and analysis of techniques to give approximate but accurate
solutions to hard problems, the variety of which is suggested by
the following.
 Advanced numerical methods are essential in making numerical weather prediction
feasible.
 Computing the trajectory of a spacecraft requires the accurate
numerical solution of a system of ordinary differential
equations.
 Car companies can improve the crash safety of their vehicles by
using computer simulations of car crashes. Such simulations
essentially consist of solving partial differential equations
numerically.
 Hedge funds (private investment
funds) use tools from all fields of numerical analysis to calculate
the value of stocks and derivatives more precisely than other
market participants.
 Airlines use sophisticated optimization algorithms to decide
ticket prices, airplane and crew assignments and fuel needs. This
field is also called operations
research.
 Insurance companies use numerical programs for actuarial analysis.
The rest of this section outlines several important themes of
numerical analysis.
History
The field of numerical analysis predates the invention of modern
computers by many centuries.
Linear
interpolation was already in use more than 2000 years ago. Many
great mathematicians of the past were preoccupied by numerical
analysis, as is obvious from the names of important algorithms like
Newton's method,
Lagrange interpolation polynomial,
Gaussian elimination, or
Euler's method.
To facilitate computations by hand, large books were produced with
formulas and tables of data such as interpolation points and
function coefficients. Using these tables, often calculated out to
16 decimal places or more for some functions, one could look up
values to plug into the formulas given and achieve very good
numerical estimates of some functions. The canonical work in the
field is the
NIST publication edited by
Abramowitz and Stegun, a
1000plus page book of a very large number of commonly used
formulas and functions and their values at many points. The
function values are no longer very useful when a computer is
available, but the large listing of formulas can still be very
handy.
The
mechanical calculator was
also developed as a tool for hand computation. These calculators
evolved into electronic computers in the 1940s, and it was then
found that these computers were also useful for administrative
purposes. But the invention of the computer also influenced the
field of numerical analysis, since now longer and more complicated
calculations could be done.
Direct and iterative methods


Direct vs iterative methods
Consider the problem of solving
 3x^{3}+4=28
for the unknown quantity
x.
+ Direct Method

  3x^{3} + 4 = 28.

 Subtract 4  3x^{3} = 24.

 Divide by 3  x^{3} = 8.

 Take cube roots  x = 2.
}
SHOULD BE NOTED THAT THE BISECTION METHOD BELOW ISN'T
EXACTLY WHAT IS DESCRIBED ON THE BISECTION PAGE
For the iterative method, apply the
bisection methodto
f(
x) =
3
x^{3} 24. The initial values are
a= 0,
b= 3,
f(
a) = 24,
f(
b)
= 57.
+ Iterative Method

 a  b  mid  f(mid)

 0  3  1.5  13.875

 1.5  3  2.25  10.17...

 1.5  2.25  1.875  4.22...

 1.875  2.25  2.0625  2.32...
}
We conclude from this table that the solution is between 1.875 and
2.0625. The algorithm might return any number in that range with an
error less than 0.2.
Discretization and numerical integration
In a two hour race, we have measured the speed of the car at three
instants and recorded them in the following table.
Time 0:20 1:00 1:40
km/h 140 150 180
A
discretizationwould be to say that the speed of
the car was constant from 0:00 to 0:40, then from 0:40 to 1:20 and
finally from 1:20 to 2:00. For instance, the total distance
traveled in the first 40 minutes is approximately (2/3h x 140
km/h)=93.3 km. This would allow us to estimate the total distance
traveled as 93.3 km + 100 km + 120 km = 313.3 km, which is an
example of
numerical integration(see below) using
a
Riemann sum, because displacement is
the
integralof velocity.
Ill posed problem: Take the function
f(
x) = 1/(
x − 1). Note that
f(1.1) = 10 and
f(1.001) = 1000: a change in
xof less than 0.1 turns into a change in
f(
x) of nearly 1000. Evaluating
f(
x) near
x= 1 is an illconditioned
problem.
Wellposed problem: By contrast, the function
f(x)=\sqrt{x}is continuous and so evaluating it is wellposed, at
least for xbeing not close to zero.
Direct methods compute the solution to a problem in a finite number
of steps. These methods would give the precise answer if they were
performed in
infinite
precision arithmetic. Examples include
Gaussian elimination, the
QR factorization method for solving [[system of linear
equationssystemsof linear equations]], and the
simplex method of
linear programming. In practice,
finite precision is used and the result is an
approximation of the true solution (assuming
stability).
In contrast to direct methods,
iterative methods are not expected to
terminate in a number of steps. Starting from an initial guess,
iterative methods form successive approximations that
converge to the exact solution only in
the limit. A
convergence
criterion is specified in order to decide when a sufficiently
accurate solution has (hopefully) been found. Even using infinite
precision arithmetic these methods would not reach the solution
within a finite number of steps (in general). Examples include
Newton's method, the
bisection method, and
Jacobi iteration. In computational matrix
algebra, iterative methods are generally needed for large
problems.
Iterative methods are more common than direct methods in numerical
analysis. Some methods are direct in principle but are usually used
as though they were not, e.g.
GMRES and the
conjugate gradient method.
For these methods the number of steps needed to obtain the exact
solution is so large that an approximation is accepted in the same
manner as for an iterative method.
Discretization
Furthermore, continuous problems must sometimes be replaced by a
discrete problem whose solution is known to approximate that of the
continuous problem; this process is called
discretization.
For example, the solution of a
differential equation is a function.
This function must be represented by a finite amount of data, for
instance by its value at a finite number of points at its domain,
even though this domain is a continuum.
The generation and propagation of errors
The study of errors forms an important part of numerical analysis.
There are several ways in which error can be introduced in the
solution of the problem.
Roundoff
Roundoff errors arise because it is
impossible to represent all
real numbers
exactly on a
finitestate
machine (which is what all practical
digital computers are).
Truncation and discretization error
Truncation errors are committed when an
iterative method is terminated or a mathematical procedure is
approximated, and the approximate solution differs from the exact
solution. Similarly, discretization induces a
discretization error because the
solution of the discrete problem does not coincide with the
solution of the continuous problem. For instance, in the iteration
in the sidebar to compute the solution of 3x^3+4=28, after 10 or so
iterations, we conclude that the root is roughly 1.99 (for
example). We therefore have a truncation error of 0.01.
Once an error is generated, it will generally propagate through the
calculation. For instance, we have already noted that the operation
+ on a calculator (or a computer) is inexact. It follows that a
calculation of the type a+b+c+d+e is even more inexact.
What does it mean when we say that the truncation error is created
when we approximate a mathematical procedure. We know that to
integrate a function exactly requires one to find the sum of
infinite trapezoids. But numerically one can find the sum of only
finite trapezoids, and hence the approximation of the mathematical
procedure. Similarly, to differentiate a function, the differential
element approaches to zero but numerically we can only choose a
finite value of the differential element.
Numerical stability and wellposed problems
Numerical stability is an
important notion in numerical analysis. An algorithm is called
numerically stable if an error, whatever its cause, does
not grow to be much larger during the calculation. This happens if
the problem is
wellconditioned, meaning that the
solution changes by only a small amount if the problem data are
changed by a small amount. To the contrary, if a problem is
illconditioned, then any small error in the data will
grow to be a large error.
Both the original problem and the algorithm used to solve that
problem can be
wellconditioned and/or
illconditioned, and any combination is possible.
So an algorithm that solves a wellconditioned problem may be
either numerically stable or numerically unstable. An art of
numerical analysis is to find a stable algorithm for solving a
wellposed mathematical problem. For instance, computing the square
root of 2 (which is roughly 1.41421) is a wellposed problem. Many
algorithms solve this problem by starting with an initial
approximation
x_{1} to \sqrt{2}, for instance
x_{1}=1.4, and then computing improved guesses
x_{2},
x_{3}, etc... One such
method is the famous
Babylonian
method, which is given by
x_{k+1} =
x_{k}/2 + 1/
x_{k}. Another
iteration, which we will call Method X, is given by
x_{k + 1} =
(
x_{k}^{2}−2)
^{2} +
x_{k}.This is a
fixed point iteration for the equation
x=(x^22)^2+x=f(x), whose solutions include \sqrt{2}. The iterates
always move to the right since f(x)\geq x. Hence
x_1=1.4<\SQRT{2}<></\SQRT{2}<>math> converges
and x_1=1.42>\sqrt{2} diverges. We have calculated a few
iterations of each scheme in table form below, with initial guesses
x_{1} = 1.4 and
x_{1} =
1.42.

Babylonian 
Babylonian 
Method X 
Method X

 x_{1} = 1.4
 x_{1} = 1.42
 x_{1} = 1.4
 x_{1} = 1.42

 x_{2} = 1.4142857...
 x_{2} = 1.41422535...
 x_{2} = 1.4016
 x_{2} = 1.42026896

 x_{3} = 1.414213564...
 x_{3} = 1.41421356242...
 x_{3} = 1.4028614...
 x_{3} = 1.42056...



 ...
 ...



 x_{1000000} = 1.41421...
 x_{28} = 7280.2284...
}
Observe that the Babylonian method converges fast regardless of the
initial guess, whereas Method X converges extremely slowly with
initial guess 1.4 and diverges for initial guess 1.42. Hence, the
Babylonian method is numerically stable, while Method X is
numerically unstable.
Areas of study
The field of numerical analysis is divided into different
disciplines according to the problem that is to be solved.
Computing values of functions

Interpolation: We have observed the temperature to
vary from 20 degrees Celsius at 1:00 to 14 degrees at 3:00. A
linear interpolation of this data would conclude that it was 17
degrees at 2:00 and 18.5 degrees at 1:30pm.
Extrapolation: If the gross domestic productof a country
has been growing an average of 5% per year and was 100 billion
dollars last year, we might extrapolate that it will be 105 billion
dollars this year.
Regression: In linear regression, given
npoints, we compute a line that passes as close as
possible to those npoints.
Optimization: Say you sell lemonade at a lemonade stand, and notice that at $1, you
can sell 197 glasses of lemonade per day, and that for each
increase of $0.01, you will sell one less lemonade per day. If you
could charge $1.485, you would maximize your profit, but due to the
constraint of having to charge a whole cent amount, charging $1.49
per glass will yield the maximum income of $220.52 per day.
Differential equation: If you set up 100 fans to
blow air from one end of the room to the other and then you drop a
feather into the wind, what happens? The feather will follow the
air currents, which may be very complex. One approximation is to
measure the speed at which the air is blowing near the feather
every second, and advance the simulated feather as if it were
moving in a straight line at that same speed for one second, before
measuring the wind speed again. This is called the Euler methodfor solving an ordinary
differential equation.
One of the simplest problems is the evaluation of a function at a
given point. The most straightforward approach, of just plugging in
the number in the formula is sometimes not very efficient. For
polynomials, a better approach is using the Horner scheme, since it reduces the necessary
number of multiplications and additions. Generally, it is important
to estimate and control roundoff
errors arising from the use of floating point arithmetic.
Interpolation, extrapolation, and regression
Interpolation solves the following
problem: given the value of some unknown function at a number of
points, what value does that function have at some other point
between the given points? A very simple method is to use linear interpolation, which assumes
that the unknown function is linear between every pair of
successive points. This can be generalized to polynomial interpolation, which is
sometimes more accurate but suffers from Runge's phenomenon. Other interpolation
methods use localized functions like spline or wavelets.
Extrapolation is very similar to
interpolation, except that now we want to find the value of the
unknown function at a point which is outside the given
points.
Regression is also similar, but
it takes into account that the data is imprecise. Given some
points, and a measurement of the value of some function at these
points (with an error), we want to determine the unknown function.
The least squaresmethod is one
popular way to achieve this.
Solving equations and systems of equations
Another fundamental problem is computing the solution of some given
equation. Two cases are commonly distinguished, depending on
whether the equation is linear or not. For instance, the equation
2x+5=3 is linear while 2x^2+5=3 is not.
Much effort has been put in the development of methods for solving
systems of linear
equations. Standard direct methods, i.e., methods that use some
matrix decomposition are
Gaussian elimination, LU decomposition, Cholesky decomposition for symmetric (or hermitian) and positivedefinite matrix, and
QR decomposition for nonsquare
matrices. Iterative methods such as
the Jacobi method, Gauss–Seidel method, successive overrelaxation and
conjugate gradient method
are usually preferred for large systems.
Rootfinding algorithms are
used to solve nonlinear equations (they are so named since a root
of a function is an argument for which the function yields zero).
If the function is differentiable and the
derivative is known, then Newton's
method is a popular choice. Linearization is another technique for solving
nonlinear equations.
Solving eigenvalue or singular value problems
Several important problems can be phrased in terms of eigenvalue decompositions or
singular value
decompositions. For instance, the spectral image compression algorithm is
based on the singular value decomposition. The corresponding tool
in statistics is called principal component
analysis.
Optimization
Optimization problems ask for the point at which a given function
is maximized (or minimized). Often, the point also has to satisfy
some constraints.
The field of optimization is further split in several subfields,
depending on the form of the objective function and the constraint.
For instance, linear programming
deals with the case that both the objective function and the
constraints are linear. A famous method in linear programming is
the simplex method.
The method of Lagrange
multipliers can be used to reduce optimization problems with
constraints to unconstrained optimization problems.
Evaluating integrals
Numerical integration, in some instances also known as numerical
quadrature, asks for the
value of a definite integral. Popular
methods use one of the Newton–Cotes formulas (like
the midpoint rule or Simpson's rule)
or Gaussian quadrature. These
methods rely on a "divide and conquer" strategy, whereby an
integral on a relatively large set is broken down into integrals on
smaller sets. In higher dimensions, where these methods become
prohibitively expensive in terms of computational effort, one may
use Monte Carlo or quasiMonte Carlo methods (see
Monte Carlo integration),
or, in modestly large dimensions, the method of sparse grids.
Differential equations
Numerical analysis is also concerned with computing (in an
approximate way) the solution of differential equations, both ordinary
differential equations and partial differential
equations.
Partial differential equations are solved by first discretizing the
equation, bringing it into a finitedimensional subspace. This can
be done by a finite element
method, a finite difference
method, or (particularly in engineering) a finite volume method. The theoretical
justification of these methods often involves theorems from
functional analysis. This
reduces the problem to the solution of an algebraic equation.
Software
Since the late twentieth century, most algorithms are implemented
in a variety of programming languages. The Netlib repository contains various collections of
software routines for numerical problems, mostly in Fortran and C. Commercial products implementing
many different numerical algorithms include the IMSL and NAG libraries; a free alternative
is the GNU Scientific
Library.
There are several popular numerical computing applications such as
MATLAB, SPLUS,
LabVIEW, and IDL as well as free and open
source alternatives such as FreeMat,
Scilab, GNU Octave
(similar to Matlab), IT++ (a C++ library),
R (similar to SPLUS) and
certain variants of Python. Performance varies
widely: while vector and matrix operations are usually fast, scalar
loops may vary in speed by more than an order of magnitude.
Many computer algebra
systems such as Mathematica also
benefit from the availability of arbitrary precision
arithmetic which can provide more accurate results.
Also, any spreadsheet software can be
used to solve simple problems relating to numerical analysis.
See also
Notes
References
 Trefethen, Lloyd N. (2006). "Numerical analysis", 20 pages. In: Timothy
Gowers and June BarrowGreen (editors), Princeton Companion of
Mathematics, Princeton University Press.
External links
General
Journals
Software and Code
Online Texts
Online Course Material
 Numerical Methods, Stuart Dalziel University of
Cambridge
 Lectures on Numerical Analysis, Dennis Deturck
and Herbert S. Wilf University of Pennsylvania
 Numerical methods, John D. Fenton University of
Karlsruhe
 Numerical
Methods for Science, Technology, Engineering and Mathematics,
Autar Kaw University of South Florida
 Numerical Analysis Project, John H.
Mathews
California State University,
Fullerton
 Numerical
Methods  Online Course, Aaron Naiman Jerusalem
College of Technology
 Lectures in Numerical Analysis, R. Radok Mahidol
University
 Introduction to Numerical Analysis for
Engineering, Henrik Schmidt Massachusetts
Institute of Technology
