Description

Regula Falsi Method

All materials on our website are shared by users. If you have any questions about copyright issues, please report us to resolve them. We are always happy to assist you.

Related Documents

Share

Transcript

The false position method or regula falsi method is a term for problem-solvingmethods in arithmetic, algebra, and calculus. In simple terms, these methods begin by attempting to evaluate a problem using test ( false ) values for the variables, and then adjust the values accordingly. Two basic types of false position method can be distinguished, simple false position and double false position. imple false position is aimed at solving problems involving direct proportion. uch problems can be written algebraically in the form! determine such thata#b,if a and b are $nown. %ouble false position is aimed at solving more di&cult problems that can be written algebraically in the form! determine such thatf()#b,if it is $nown thatf(')#b',f()#b.%ouble false position is mathematically euivalent to linear interpolation* for an a&ne linear function,f()#a+c,it provides the eact solution, while for a nonlinear function f it provides an approimation that can be successively improved by iteration.In problems involving arithmetic or algebra, the false position method or regula falsi is used to refer to basic trial and error methods of solving problems by substituting test values for the un$nown uantities. This is sometimes also referred to as guess and chec$ . ersions of this method predate the advent of algebra and the use of euations.or simple false position, the method of solving what we would now write as a # b begins by using a test input value , and /nding the corresponding output value b by multiplication! a # b. The correct answer is then found
by proportional adjustment, # 0 b 1 b. This techniue is found in cuneiform tablets from ancient 2abylonian mathematics, and possibly in papyri from ancient 3gyptian mathematics.4'56i$ewise, double false position arose in late antiuity as a purely arithmetical algorithm. It was used mostly to solve what are now called a&ne linear problems by using a pair of test inputs and the corresponding pair of outputs. This algorithm would be memori7ed and carried out by rote. In the ancient 8hinese mathematical tet called The 9ine 8hapters on the :athematical ;rt (
九章算術
), dated from 200 BC to AD 100, most of Chapter 7 was devoted to the algorithm. There, the procedure was ustified ! co#crete arithmetical argume#ts, the# applied creativel to a wide variet of stor pro!lems, i#cludi#g o#e i#volvi#g what we would call seca#t li#es o# a $uadratic pol #omial. A more t pical e%ample is this &oi#t purchase& pro!lem'(ow a# item is purchased oi#tl ever o#e co#tri!utes * +coi#s, the e%cess is - ever o#e co#tri!utes 7, the deficit is . Tell' The #um!er of people, the item price, what is each/ A#swer' 7 people, item price -.+2Betwee# the th a#d 10th ce#turies, the g ptia# 3uslim mathematicia# A!u 4amil wrote a #ow5lost treatise o# the use of dou!le false positio#, 6#ow# as the Boo6 of the Two rrors 4it
<b al-$ha=<>ayn). The oldest surviving writing on double false position from the :iddle 3ast is that of ?usta ibn 6ua ('@th century), a 8hristian ;rab mathematician from 2aalbe$, 6ebanon. Ae justi/edthe techniue by a formal, 3uclidean-style geometric proof. Bithin the tradition of medieval :uslim mathematics, double false position was $nown as his<b al-$ha=<>ayn ( rec$oning by two errors ). It was used for centuries, especially in the :aghreb, to solve practical problems such as commercial and juridical uestions (estate partitions according to rules of ?uranic inheritance), as well as purely recreational problems. The algorithm was oftenmemori7ed with the aid of mnemonics, such as a verse attributed to Ibn al- Casamin and balance-scale diagrams eplained by al-Aassar and Ibn al-2anna, all three being mathematicians of :oroccan srcin.4D56eonardo of Eisa (ibonacci) devoted 8hapter 'D of his boo$ 6iber ;baci (;% '@) to eplaining and demonstrating the uses of double false position, terming the method regulis elchatayn after the al-$ha=<>ayn method that he had learned from ;rab sources.4D5
9umerical analysis4edit5In numerical analysis, double false position became a root-/nding algorithm that combines features from the bisection method and the secant method. The /rst two iterations of the false position method. The red curve shows the function f and the blue lines are the secants.6i$e the bisection method, the false position method starts with two points a@and b@ such that f(a@) and f(b@) are of opposite signs, which implies by the intermediate value theorem that the function f has a root in the interval 4a@, b@5, assuming continuity of the function f. The method proceeds by producinga seuence of shrin$ing intervals 4a$, b$5 that all contain a root of f.;t iteration number $, the numberc$#b$Ff(b$)(b$Fa$)f(b$)Ff(a$)is computed. ;s eplained below, c$ is the root of the secant line through (a$,f(a$)) and (b$, f(b$)). If f(a$) and f(c$) have the same sign, then we set a$+' # c$ and b$+' # b$, otherwise we set a$+' # a$ and b$+' # c$. This process is repeated until the root is approimated su&ciently well. The above formula is also used in the secant method, but the secant method always retains the last two computed points, while the false position method retains two points which certainly brac$et a root. Gn the other hand, the only diHerence between the false position method and the bisection method is that the latter uses c$ # (a$ + b$) .inding the root of the secant4edit5Jiven a$ and b$, we construct the line through the points (a$, f(a$)) and (b$, f(b$)), as demonstrated in the picture immediately above. 9ote that this line is a secant or chord of the graph of the function f. In point-slope form, it can be de/ned as
yFf(b$)#f(b$)Ff(a$)b$Fa$(Fb$).Be now choose c$ to be the root of this line (substituting for ), and setting y#@ and see thatf(b$)+f(b$)Ff(a$)b$Fa$(c$Fb$)#@.olving this euation gives the above euation for c$.;nalysis4edit5If the initial end-points a@ and b@ are chosen such that f(a@) and f(b@) are of opposite signs, then at each step, one of the end-points will get closer to a root of f. If the second derivative of f is of constant sign (so there is no inKection point) in the interval, then one endpoint (the one where f also has the same sign) will remain /ed for all subseuent iterations while the converging endpoint becomes updated. ;s a result, unli$e the bisection method, the width of the brac$et does not tend to 7ero (unless the 7ero is at an inKection point around which sign(f)#-sign(fL)). ;s a conseuence, the linear approimation to f(), which is used to pic$ the false position, does not improve in its uality.Gne eample of this phenomenon is the functionf()#DFM+Don the initial brac$et 4F','5. The left end, F', is never replaced (after the /rstthree iterations, fL is negative on the interval) and thus the width of the brac$et never falls below '. Aence, the right endpoint approaches @ at a linear rate (the number of accurate digits grows linearly, with a rate of convergence of D).or discontinuous functions, this method can only be epected to /nd a point where the function changes sign (for eample at #@ for ' or the sign function). In addition to sign changes, it is also possible for the method to converge to a point where the limit of the function is 7ero, even if the functionis unde/ned (or has another value) at that point (for eample at #@ for the function given by f()#abs()-N when O@ and by f(@)#P, starting with the

We Need Your Support

Thank you for visiting our website and your interest in our free products and services. We are nonprofit website to share and download documents. To the running of this website, we need your help to support us.

Thanks to everyone for your continued support.

No, Thanks