(c. 1800–1600 BC) with annotations. The approximation of the square root of 2
is four sexagesimal
figures, which is about six decimal
figures. 1 + 24/60 + 51/602
Numerical analysis is the study of algorithm
s that use numerical approximation
(as opposed to symbolic manipulations
) for the problems of mathematical analysis
(as distinguished from discrete mathematics
). Numerical analysis naturally finds application in all fields of engineering and the physical sciences, but in the 21st century also the life sciences, social sciences, medicine, business and even the arts have adopted elements of scientific computations. The growth in computing power has revolutionized the use of realistic mathematical models in science and engineering, and subtle numerical analysis is required to implement these detailed models of the world. For example, ordinary differential equation
s appear in celestial mechanics
(predicting the motions of planets, stars and galaxies); numerical linear algebra
is important for data analysis; stochastic differential equation
s and Markov chain
s are essential in simulating living cells for medicine and biology.
Before the advent of modern computers, numerical method
s often depended on hand interpolation
formulas applied to data from large printed tables. Since the mid 20th century, computers calculate the required functions instead, but many of the same formulas nevertheless continue to be used as part of the software algorithms.
[Brezinski, C., & Wuytack, L. (2012). Numerical analysis: Historical developments in the 20th century. Elsevier.]
The numerical point of view goes back to the earliest mathematical writings. A tablet from the Yale Babylonian Collection
), gives a sexagesimal
numerical approximation of the square root of 2
, the length of the diagonal
in a unit square
Numerical analysis continues this long tradition: rather than exact symbolic answers, which can only be applied to real-world measurements by translation into digits, it gives approximate solutions within specified error bounds.
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
* 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 equation
* Hedge fund
s (private investment funds) use tools from all fields of numerical analysis to attempt to calculate the value of stock
s and derivatives
more precisely than other market participants.
* Airlines use sophisticated optimization algorithms to decide ticket prices, airplane and crew assignments and fuel needs. Historically, such algorithms were developed within the overlapping field of operations research
* Insurance companies use numerical programs for actuarial
The rest of this section outlines several important themes of numerical analysis.
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 1000-plus 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
Consider the problem of solving
+ 4 = 28
for the unknown quantity ''x''.
For the iterative method, apply the bisection method
to ''f''(''x'') = 3''x''3
− 24. The initial values are ''a'' = 0, ''b'' = 3, ''f''(''a'') = −24, ''f''(''b'') = 57.
From this table it can be concluded 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, the speed of the car is measured at three instants and recorded in the following table.
A discretization would 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 . This would allow us to estimate the total distance traveled as + + = , which is an example of numerical integration (see below) using a Riemann sum
, because displacement is the integral
Ill-conditioned problem: Take the function . Note that ''f''(1.1) = 10 and ''f''(1.001) = 1000: a change in ''x'' of less than 0.1 turns into a change in ''f''(''x'') of nearly 1000. Evaluating ''f''(''x'') near ''x'' = 1 is an ill-conditioned problem.
Well-conditioned problem: By contrast, evaluating the same function near ''x'' = 10 is a well-conditioned problem. For instance, ''f''(10) = 1/9 ≈ 0.111 and ''f''(11) = 0.1: a modest change in ''x'' leads to a modest change in ''f''(''x'').
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 systems of 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 method
s are not expected to terminate in a finite 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 test, often involving the residual
, 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.
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
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.
s arise because it is impossible to represent all real number
s exactly on a machine with finite memory (which is what all practical digital computer
Truncation and discretization error
s 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
, after 10 or so iterations, it can be concluded that the root is roughly 1.99 (for example). Therefore, there is a truncation error of 0.01.
Once an error is generated, it will generally propagate through the calculation. For instance, already noted is that the operation + on a calculator (or a computer) is inexact. It follows that a calculation of the type is even more inexact.
The truncation error is created when a mathematical procedure is approximated. To integrate a function exactly it is required to find the sum of infinite trapezoids, but numerically only the sum of only finite trapezoids can be found, and hence the approximation of the mathematical procedure. Similarly, to differentiate a function, the differential element approaches zero but numerically only a finite value of the differential element can be chosen.
Numerical stability and well-posed problems
is a 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.
[Higham, N. J. (2002). Accuracy and stability of numerical algorithms (Vol. 80). SIAM.]
This happens if the problem is 'well-conditioned
', 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 'ill-conditioned', 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 'well-conditioned' or 'ill-conditioned', and any combination is possible.
So an algorithm that solves a well-conditioned problem may be either numerically stable or numerically unstable. An art of numerical analysis is to find a stable algorithm for solving a well-posed mathematical problem. For instance, computing the square root of 2 (which is roughly 1.41421) is a well-posed problem. Many algorithms solve this problem by starting with an initial approximation ''x''0
, for instance ''x''0
= 1.4, and then computing improved guesses ''x''1
, etc. One such method is the famous Babylonian method
, which is given by ''x''''k''+1
''/2 + 1/''xk
''. Another method, called 'method X', is given by ''x''''k''+1
. A few iterations of each scheme are calculated in table form below, with initial guesses ''x''0
= 1.4 and ''x''0
Observe that the Babylonian method converges quickly regardless of the initial guess, whereas Method X converges extremely slowly with initial guess ''x''0
= 1.4 and diverges for initial guess ''x''0
= 1.42. Hence, the Babylonian method is numerically stable, while Method X is numerically unstable.
:Numerical stability is affected by the number of the significant digits the machine keeps on, if a machine is used that keeps only the four most significant decimal digits, a good example on loss of significance can be given by these two equivalent functions
:Comparing the results of
: by comparing the two results above, it is clear that loss of significance
(caused here by catastrophic cancellation
from subtracting approximations to the nearby numbers
, despite the subtraction's being computed exactly) has a huge effect on the results, even though both functions are equivalent, as shown below
: The desired value, computed using infinite precision, is 11.174755...
* The example is a modification of one taken from Mathew; Numerical methods using Matlab, 3rd ed.
Areas of study
The field of numerical analysis includes many sub-disciplines. Some of the major ones are:
Computing values of functions
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 round-off error
s arising from the use of floating point
Interpolation, extrapolation, and regression
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?
is very similar to interpolation, except that now the value of the unknown function at a point which is outside the given points must be found.
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), the unknown function can be found. The least squares
-method is one 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
is linear while
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
) and positive-definite matrix
, and QR decomposition
for non-square matrices. Iterative methods such as the Jacobi method
, Gauss–Seidel method
, successive over-relaxation
and conjugate gradient method
are usually preferred for large systems. General iterative methods can be developed using a matrix splitting
s 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 decomposition
s or singular value decomposition
s. 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 problems ask for the point at which a given function is maximized (or minimized). Often, the point also has to satisfy some constraint
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.
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 quasi-Monte Carlo method
s (see Monte Carlo integration
), or, in modestly large dimensions, the method of sparse grid
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 finite-dimensional 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.
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
. Commercial products implementing many different numerical algorithms include the IMSL
libraries; a free-software
alternative is the GNU Scientific Library
Over the years the Royal Statistical Society
published numerous algorithms in its ''Applied Statistics''
(code for these "AS" functions ihere
similarly, in its ''Transactions on Mathematical Software
'' ("TOMS" code ihere
The Naval Surface Warfare Center
several times published it''Library of Mathematics Subroutines''
There are several popular numerical computing applications such as MATLAB
[Gander, W., & Hrebicek, J. (Eds.). (2011). Solving problems in scientific computing using Maple and Matlab®. Springer Science & Business Media.] [Barnes, B., & Fulford, G. R. (2011). Mathematical modelling with case studies: a differential equations approach using Maple and MATLAB. Chapman and Hall/CRC.] TK Solver
, and IDL
as well as free and open source alternatives such as FreeMat
, GNU Octave
(similar to Matlab), and IT++
(a C++ library). There are also programming languages such as R
(similar to S-PLUS) and Python
with libraries such as NumPy
. 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 system
s such as Mathematica
also benefit from the availability of arbitrary-precision arithmetic
which can provide more accurate results.
[Marasco, A., & Romano, A. (2001). Scientific Computing with Mathematica: Mathematical Problems for Ordinary Differential Equations; with a CD-ROM. Springer Science & Business Media.]
Also, any spreadsheet software
can be used to solve simple problems relating to numerical analysis.
, for example, has hundreds of available functions
, including for matrices, which may be used in conjunction with its built in "solver"
*Analysis of algorithms
*List of numerical analysis topics
*Local linearization method
* (examples of the importance of accurate arithmetic).
* Trefethen, Lloyd N.
20 pages. In: Timothy Gowers and June Barrow-Green (editors), ''Princeton Companion of Mathematics'', Princeton University Press.
''Numerische Mathematik'', volumes 1-66, Springer, 1959-1994 (searchable; pages are images).
'', volumes 1–112, Springer, 1959–2009
*''Journal on Numerical Analysis
'', volumes 1-47, SIAM, 1964–2009
William H. Press (free, downloadable previous editions)
), R.J.Hosking, S.Joe, D.C.Joyce, and J.C.Turner''CSEP'' (Computational Science Education Project) U.S. Department of Energy
) Numerical Methods
ch 3. in the ''Digital Library of Mathematical Functions
Online course materialNumerical Methods
, Stuart Dalziel University of CambridgeLectures on Numerical Analysis
Dennis Deturck and Herbert S. Wilf University of PennsylvaniaNumerical methods
John D. Fenton University of KarlsruheNumerical Methods for Physicists
Anthony O’Hare Oxford UniversityLectures in Numerical Analysis
), R. Radok Mahidol UniversityIntroduction to Numerical Analysis for Engineering
Henrik Schmidt Massachusetts Institute of Technology''Numerical Analysis for Engineering''
D. W. Harder University of Waterloo