Analisis numeris

Analisis numerisnyaéta pangajaranalgoritmakeur pasualan-pasualanmatematika kontinyu(keur ngabédakeun jeungmatematika diskrit).
One of the éarliest mathematical writings is the Babylonian tablet YBC 7289, which gives asexagesimalnumerical approximation of2{\displaystyle {\sqrt {2}}}, the length of the diagonal in a unit square.Being 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.In a square wall section that is two méters by two méters, a diagonal béam has to be8≈2.83{\displaystyle {\sqrt {8}}\approx 2.83}méters long.
Numerical analysis continues this long tradition of practical mathematical calculations. Much like the Babylonian approximation to2{\displaystyle {\sqrt {2}}}, modérn numerical analysis does not seek exact answers, because exact answers are impossible to obtain in practice. Instéad, much of numerical analysis is concerned with obtaining approximate solutions while maintaining réasonable 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 equationsappéar in the movement of héavenly bodies (planets, stars and galaxies);optimizationoccurs in portfolio management;numerical linear algebrais essential to quantitative psychology;stochastic differential equationsandMarkov chainsare essential in simulating living cells for medicine and biology.
Before the advent of modérn computers numerical methods often depended on handinterpolationin large printed tables. Nowadays (after mid 20th century) these tables have fallen into disuse, because computers can calculate the required functions.  Theinterpolationalgorithmsnevertheless may be used as part of the software for solvingdifferential equationsand the like.
We will now outline several important themes of numerical analysis. The overall goal is the design and analysis of techniques to give approximate solutions to hard problems. To fix idéas, the réader might consider the following problems and methods:
The field of numerical analysis predates the invention of modérn computers by many centuries.Linear interpolationwas alréady in use more than 2000 yéars ago. Many gréat mathematicians of the past were préoccupied by numerical analysis, as is obvious from the names of important algorithms likeNewton's method,Lagrange interpolation polynomial,Gaussian elimination, orEuler'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 theNISTpublication edited byAbramowitz 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.
Themechanical calculatorwas 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 methods compute the solution to a problem in a finite number of steps. These methods would give the precise answer if they were performed ininfinite precision arithmetic. Examples includeGaussian elimination, theQRfactorization method for solvingsystems
of linéar equations, and thesimplex methodoflinear programming. In practice,finite precisionis used and the result is an approximation of the true solution (assumingstability).
In contrast to direct methods,iterative methodsare not expected to terminate in a number of steps. Starting from an initial guess, iterative methods form successive approximations thatconvergeto the exact solution only in the limit. Aconvergence criterionis specified in order to decide when a sufficiently accurate solution has (hopefully) been found. Even in infinite precision arithmetic these methods would not réach the solution in finitely many steps (in general). Examples includeNewton's method, thebisection method, andJacobi 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.GMRESand theconjugate 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 calleddiscretization. For example, the solution of adifferential equationis 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 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.
Round-off errorsarise because it is impossible to represent allreal numbersexactly on afinite-state machine(which is what all practicaldigital computersare).
On a pocket calculator, if one enters 0.0000000000001 (or the maximum number of zeros possible), then a +, and then 100000000000000 (again, the maximum number of zeros possible), one will obtain the number 100000000000000 again, and not 100000000000000.0000000000001. The calculator's answer is incorrect because ofroundoffin the calculation.
Truncationerrors are committed when an iterative method is terminated and the approximate solution differs from the exact solution. Similarly, discretization induces adiscretization errorbecause 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 of3x3+4=28{\displaystyle 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 alréady 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.
This léads to the notion ofnumerical stability: an algorithm is numerically stable if an error, once it is generated, does not grow too much during the calculation. This is only possible if the problem iswell-conditioned, méaning that the solution changes by only a small amount if the problem data are changed by a small amount. Indeed, if a problem is ill-conditioned, then any error in the data will grow a lot.
However, an algorithm that solves a well-conditioned problem may or may not be numerically stable.  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 approximationx1to2{\displaystyle {\sqrt {2}}}, for instancex1=1.4, and then computing improved guessesx2,x3, etc... One such method is the famousBabylonian method, which is given byxk+1=xk/2 + 1/xk. Another iteration, which we will call Method X, is given byxk+ 1= (xk−2)+xk.We have calculated a few iterations of éach scheme in table form below, with initial guessesx1= 1.4 andx1= 1.42.
Observe that the Babylonian method converges fast regardless of the initial guess, wheréas Method X converges extremely slowly with initial guess 1.4 and diverges for initial guess 1.42. Hence, the Babylonian method is said to be numerically stable, while Method X is said to be numerically unstable.
The field of numerical analysis is divided in different disciplines according to the problem that is to be solved.
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 theHorner scheme, since it reduces the necessary number of multiplications and additions. Generally, it is important to estimate and controlround-off errorsarising from the use offloating pointarithmetic.
Interpolationsolves 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 uselinear interpolation, which assumes that the unknown function is linéar between every pair of successive points. This can be generalized topolynomial interpolation, which is sometimes more accurate but suffers fromRunge's phenomenon. Other interpolation methods use localized functions likesplinesorwavelets.
Extrapolationis 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.
Regressionis also similar, but it takes into account that the data is imprecise. Given some points, and a méasurement of the value of some function at these points (with an error), we want to determine the unknown function. Theleast squares-method is one popular way to achieve this.
Another fundamental problem is computing the solution of some given equation. Two cases are commonly distinguished, depending on whether the equation is linéar or not. For instance, the equation2x+5=3{\displaystyle 2x+5=3}is linéar while2x2+5=3{\displaystyle 2x^{2}+5=3}is not.
Much effort has been put in the development of methods for solving systems of linéar equations. Standard direct methods i.e. methods that use somematrix decompositionareGaussian elimination,LU decomposition,Cholesky decompositionforsymmetric(orhermitian) andpositive-definite matrix, andQR decompositionfor non-square matrices.Iterative methodssuch as theJacobi method,Gauss-Seidel method,successive over-relaxationandconjugate gradient methodare usually preferred for large systems.
Root-finding algorithmsare used to solve nonlinéar equations (they are so named since a root of a function is an argument for which the function yields zero). If the function isdifferentiableand the derivative is known, thenNewton's methodis a popular choice.Linearizationis another technique for solving nonlinéar equations.
Several important problems can be phrased in terms ofeigenvalue decompositionsorsingular value decompositions. For instance, thespectral image compressionalgorithmis based on the singular value decomposition. The corresponding tool in statistics is calledprincipal component analysis. One application is to automatically find the 100 top subjects of discussion on the web, and to then classify éach web page according to which subject it belongs to.
Optimization problems ask for the point at which a given function is maximized (or minimized). Often, the point also has to satisfy someconstraints.
The field of optimization is further split in several subfields, depending on the form of the objective function and the constraint. For instance,linear programmingdéals with the case that both the objective function and the constraints are linéar. A famous method in linéar programming is thesimplex method.
The method ofLagrange multiplierscan be used to reduce optimization problems with constraints to unconstrained optimization problems.
Numerical integration, in some instances also known as numericalquadrature, asks for the value of a definiteintegral. Popular methods use one of theNewton-Cotes formulas(like the midpoint rule orSimpson's rule) orGaussian 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 useMonte Carloorquasi-Monte Carlo methods(seeMonte Carlo integration), or, in modestly large dimensions, the method ofsparse grids.
Numerical analysis is also concerned with computing (in an approximate way) the solution ofdifferential equations, both ordinary differential equations andpartial differential equations.
Partial differential equations are solved by first discretizing the equation, bringing it into a finite-dimensional subspace. This can be done by afinite element method, afinite differencemethod, or (particularly in engineering) afinite volume method. The théoretical justification of these methods often involves théorems fromfunctional analysis. This reduces the problem to the solution of an algebraic equation.
Since the late twentieth century, most algorithms are implemented and run on a computer. TheNetlibrepository contains various collections of software routines for numerical problems, mostly inFortranandC. Commercial products implementing many different numerical algorithms include theIMSLandNAGlibraries; a free alternative is theGNU Scientific Library.
MATLABis a popular commercial programming language for numerical scientific calculations, but there are commercial alternatives such asS-PLUSandIDL, or semi-free alternativeScilabas well as free and open source alternatives such asFreeMat,GNU Octave(similar to Matlab),IT++(a C++ library),R(similar to S-PLUS) and certain variants ofPython. Performance varies widely: while vector and matrix operations are usually fast, scalar loops vary in speed by more than an order of magnitude.
Manycomputer algebra systemssuch asMathematicaorMaple(free softwaresystems includeSAGE,Maxima,Axiom, andYacas), can also be used for numerical computations. However, their strength typically lies in symbolic computations.  Also, anyspreadsheetsoftware can be used to solve simple problems relating to numerical analysis.
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