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1 > 2. Error and Computer Arithmetic Numerical analysis is concerned with how to solve a problem numerically, i.e., how to develop a sequence of numerical calculations to get a satisfactory answer. Part of this process is the consideration of the errors that arise in these calculations, from the errors in the arithmetic operations or from other sources.

2 > 2. Error and Computer Arithmetic Computers use binary arithmetic, representing each number as a binary number: a finite sum of integer powers of 2. Some numbers can be represented exactly, but others, such as 1 10, 1 100, ,..., cannot. For example, = has an exact representation in binary (base 2), but does not. And, of course, there are the transcendental numbers like π that have no finite representation in either decimal or binary number system.

3 > 2. Error and Computer Arithmetic Computers use 2 formats for numbers. Fixed-point numbers are used to store integers. Typically, each number is stored in a computer word of 32 binary digits (bits) with values of 0 and 1. at most 2 32 different numbers can be stored. If we allow for negative numbers, we can represent integers in the range 2 31 x , since there are 2 32 such numbers. Since , the range for fixed-point numbers is too limited for scientific computing. = always get an integer answer. the numbers that we can store are equally spaced. very limited range of numbers. Therefore they are used mostly for indices and counters. An alternative to fixed-point, floating-point numbers approximate real numbers. the numbers that we can store are NOT equally spaced. wide range of variably-spaced numbers that can be representeed exactly.

4 > 2. Error and Computer Arithmetic > 2.1 Floating-point numbers Numbers must be stores and used for arithmetic operations. Storing: 1 integer format 2 floating-point format Definition (decimal Floating-point representation) Let consider x 0 written in decimal system. Then it can be written uniquely as where σ = +1 or 1 is the sign e is an integer, the exponent 1 x < 10, the significand or mantissa Example ( = ( } {{ } ) 10 2 ) x x = σ x 10 e (4.1) σ = +1, the exponent e = 2, the significand x =

5 > 2. Error and Computer Arithmetic > 2.1 Floating-point numbers The decimal floating-point representation of x R is given in (4.1), with limitations on the 1 number of digits in mantissa x 2 size of e Example Suppose we limit 1 number of digits in x to e 99 We say that a computer with such a representation has a four-digit decimal floating point arithmetic. This implies that we cannot store accurately more than the first four digits of a number; and even the fourth digit may be changed by rounding. What is the next smallest number bigger than 1? What is the next smallest number bigger than 100? What are the errors and relative errors? What is the smallest positive number?

6 > 2. Error and Computer Arithmetic > 2.1 Floating-point numbers Definition (Floating-point representation of a binary number x) Let consider x written in binary format. Analogous to (4.1) x = σ x 2 e (4.2) where σ = +1 or 1 is the sign e is an integer, the exponent x is a binary fraction satisfying (1) 2 x < (10) 2 (in decimal:1 x < 2) For example, if x = ( ) 2 then σ = +1, e = 4 = (100) 2 and x = ( ) 2

7 > 2. Error and Computer Arithmetic > 2.1 Floating-point numbers The floating-point representation of a binary number x is given by (4.2) with a restriction on 1 number of digits in x: the precision of the binary floating-point representation of x 2 size of e The IEEE floating-point arithmetic standard is the format for floating point numbers used in almost all computers. the IEEE single precision floating-point representation of x has a precision of 24 binary digits, and the exponent e is limited by 126 e 127: where, in binary x = σ (1.a 1 a 2... a 23 ) 2 e ( ) 2 e ( ) 2 the IEEE double precision floating-point representation of x has a precision of 53 binary digits, and the exponent e is limited by 1022 e 1023: x = σ (1.a 1 a 2... a 52 ) 2 e

8 > 2. Error and Computer Arithmetic > 2.1 Floating-point numbers the IEEE single precision floating-point representation of x has a precision of 24 binary digits, and the exponent e is limited by 126 e 127: stored on 4 bytes (32 bits) b 1 }{{} σ x = σ (1.a 1 a 2... a 23 ) 2 e b 2 b 3 b 9 } {{ } E=e+127 b 10 b 11 b 32 } {{ } x the IEEE double precision floating-point representation of x has a precision of 53 binary digits, and the exponent e is limited by 1022 e 1023: stored on 8 bytes (64 bits) x = σ (1.a 1 a 2... a 52 ) 2 e b 1 }{{} σ b 2 b 3 b 12 } {{ } E=e+1023 b 13 b 14 b 64 } {{ } x

9 > 2. Error and Computer Arithmetic > Accuracy of floating-point representation Epsilon machine How accurate can a number be stored in the floating point representation? How can this be measured? (1) Machine epsilon Machine epsilon For any format, the machine epsilon is the difference between 1 and the next larger number that can be stored in that format. In single precision IEEE, the next larger binary number is }{{} a 23 ( cannot be stored exactly) Then the machine epsilon in single precision IEEE format is 2 23 = i.e., we can store approximately 7 decimal digits of a number x in decimal format.

10 > 2. Error and Computer Arithmetic > Accuracy of floating-point representation Then the machine epsilon in double precision IEEE format is 2 52 = IEEE double-precision format can be used to store approximately 16 decimal digits of a number x in decimal format. MATLAB: machine epsilon is available as the constant eps.

11 > 2. Error and Computer Arithmetic > Accuracy of floating-point representation Another way to measure the accuracy of floating-point format: (2) look for the largest integer M such that any integer x such that 0 x M can be stored and represented exactly in floating point form. If n is the number of binary digits in the significand x, all integers less or equal to ( ( ) 2 2 n 1 = (n 1)) 2 n 1 ( 1 1 ) 2 = n n 1 = 2 n 1 2 can be represented exactly. In IEEE single precision format M = 2 24 = and all 7-digit decimal integers will store exactly. In IEEE double precision format M = 2 53 = and all 15-digit decimal integers and most 16 digit ones will store exactly.

12 > 2. Error and Computer Arithmetic > Rounding and chopping Let say that a number x has a significand x = 1.a 1 a 2... a n 1 a n a n+1 but the floating-point representation may contain only n binary digits. Then x must be shortened when stored. Definition We denote the machine floating-point version of x by fl(x). 1 truncate or chop x to n binary digits, ignoring the remaining digits 2 round x to n binary digits, based on the size of the part of x following digit n: (a) if digit n + 1 is 0, chop x to n digits (b) if digit n + 1 is 1, chop x to n digits and add 1 to the last digit of the result

13 > 2. Error and Computer Arithmetic > Rounding and chopping It can be shown that where ɛ is a small number depending of x. (a) If chopping is used (b) If rounding is used Characteristics of chopping: fl(x) = x (1 + ɛ) (4.3) 2 n+1 ɛ 0 (4.4) 2 n ɛ 2 n (4.5) 1 the worst possible error is twice as large as when rounding is used 2 the sign of the error x fl(x) is the same as the sign of x The worst of the two: no possibility of cancellation of errors. Characteristics of rounding: 1 the worst possible error is only half as large as when chopping is used 2 More important: the error x fl(x) is negative for only half the cases, which leads to better error propagation behavior

14 > 2. Error and Computer Arithmetic > Rounding and chopping For single precision IEEE floating-point rounding arithmetic (there are n = 24 digits in the significand): 1 chopping ( rounding towards zero ): 2 23 ɛ 0 (4.6) 2 standard rounding: 2 24 ɛ 2 24 (4.7) For double precision IEEE floating-point rounding arithmetic: 1 chopping: 2 52 ɛ 0 (4.8) 2 rounding: 2 53 ɛ 2 53 (4.9)

15 > 2. Error and Computer Arithmetic >Conseq. for programming of floating-point arithm. Numbers that have finite decimal expressions may have infinite binary expansions. For example (0.1) 10 = ( ) 2 Hence (0.1) 10 cannot be represented exactly in binary floating-point arithmetic. Possible problems: Run into infinite loops pay attention to the language used: Fortran and C have both single and double precision, specify double precision constants correctly MATLAB does all computations in double precision

16 > 2. Error and Computer Arithmetic > 2.2 Errors: Definitions, Sources and Examples Definition The error in a computed quantity is defined as Error(x A ) = x T - x A where x T =true value, x A =approximate value. This is called also absolute error. The relative error Rel(x A ) is a measure off error related to the size of the true value Rel(x A ) = true error value = x T x A x T For example, for the approximation π = 22 7 we have x T = π = and x A = 22 7 = ( ) 22 Error = π ( ) 22 Rel = π π = =

17 > 2. Error and Computer Arithmetic > 2.2 Errors: Definitions, Sources and Examples The notion of relative error is a more intrinsic error measure. 1 The exact distance between 2 cities: x 1 T = 100km and the measured distance is x 1 A = 99km Error ( x 1 ) T = x 1 T x 1 A = 1km Rel ( x 1 ) Error ( x 1 ) T = T = 0.01 = 1% x 1 T 2 The exact distance between 2 cities: x 2 T distance is x 2 A = 1km = 2km and the measured Error ( x 2 ) T = x 2 T x 2 A = 1km Rel ( x 2 ) Error ( x 2 ) T = T = 0.5 = 50% x 2 T

18 > 2. Error and Computer Arithmetic > 2.2 Errors: Definitions, Sources and Examples Definition (significant digits) The number of significant digits in x A is number of its leading digits that are correct relative to the corresponding digits in the true value x T More precisely, if x A, x T are written in decimal form; compute the error x T = a 1 a 2.a 3 a m a m+1 a m+2 x T x A = b m+1 b m+2 If the error is 5 units in the (m + 1) th digit of x T, counting rightward from the first nonzero digit, then we say that x A has, at least, m significant digits of accuracy relative to x T. Example 1 x A = 0.222, x T = 2 9 : x T = x T x A =

19 > 2. Error and Computer Arithmetic > 2.2 Errors: Definitions, Sources and Examples Example 1 x A = , x T = : x T = x T x A = x A = , x T = : x T = x T x A = x A = 22 7 = , x T = π = : x T = x T x A =

20 > 2. Error and Computer Arithmetic > Sources of error Errors in a scientific-mathematical-computational problem: 1 Original errors (E1) Modeling errors (E2) Blunders and mistakes (E3) Physical measurement errors (E4) Machine representation and arithmetic errors (E5) Mathematical approximation errors 2 Consequences of errors (F1) Loss-of-significance errors (F2) Noise in function evaluation (F3) Underflow and overflow erros

21 > 2. Error and Computer Arithmetic > Sources of error (E1) Modeling errors: the mathematical equations are used to represent physical reality - mathematical model. Malthusian growth model (it can be accurate for some stages of growth of a population, with unlimited resources) N(t) = N 0 e kt, N 0, k 0 where N(t) = population at time t. For large t the model overestimates the actual population: accurately models the growth of US population for 1790 t 1860, with k = , N 0 = 3, 929, 000 e 1790k but considerably overestimates the actual population for 1870

22 > 2. Error and Computer Arithmetic > Sources of error (E2) Blunders and mistakes: mostly programming errors test by using cases where you know the solution break into small subprograms that can be tested separately (E2) Physical measurement errors. For example, the speed of light in vacuum c = ( ɛ) cm/sec, ɛ Due to the error in data, the calculations will contain the effect of this observational error. Numerical analysis cannot remove the error in the data, but it can look at its propagated effect in a calculation and suggest the best form for a calculation that will minimize the propagated effect of errors in data

23 > 2. Error and Computer Arithmetic > Sources of error (E4) Machine representation and arithmetics errors. For example errors from rounding and chopping. they are inevitable when using floating-point arithmetic they form the main source of errors with some problems (solving systems of linear equations). We will look at the effect of rounding errors for some summation procedures (E4) Mathematical approximation errors: major forms of error that we will look at. For example, when evaluating the integral I = 1 0 e x2 dx, since there is no antiderivative for e x2, I cannot be evaluated explicitly. Instead, we approximate it with a quantity that can be computed.

24 > 2. Error and Computer Arithmetic > Sources of error Using the Taylor approximation we can easily evaluate I e x2 1 x 2 + x4 2! x6 3! + x8 4! 1 0 ) (1 x 2 + x4 2! x6 3! + x8 dx 4! with the truncation error being evaluated by (3.10)

25 > 2. Error and Computer Arithmetic > Loss-of-significance errors Loss of significant digits Example Consider the evaluation of f(x) = x[ x + 1 x] for an increasing sequence of values of x. x 0 Computed f(x) True f(x) , , Table: results of using a 6-digit decimal calculator As x increases, there are fewer digits of accuracy in the computed value f(x)

26 > 2. Error and Computer Arithmetic > Loss-of-significance errors What happens? For x = 100: 100 = } {{ }, exact 101 = } {{ } rounded where 101 is correctly rounded to 6 significant digits of accuracy. x + 1 x = = while the true value should be The calculation has a loss-of-significance error. Three digits of accuracy in x + 1 = 101 were canceled by subtraction of the corresponding digits in x = 100. The loss of accuracy was a by-product of the form of f(x) and the finite precision 6-digit decimal arithmetic being used

27 > 2. Error and Computer Arithmetic > Loss-of-significance errors For this particular f, there is a simple way to reformulate it and avoid the loss-of-significance error: f(x) = x x + 1 x which on a 6 digit decimal calculator will imply the correct answer to six digits. f(100) =

28 > 2. Error and Computer Arithmetic > Loss-of-significance errors Example Consider the evaluation of f(x) = 1 cos(x) x 2 for a sequence approaching 0. x 0 Computed f(x) True f(x) Table: results of using a 10-digit decimal calculator

29 > 2. Error and Computer Arithmetic > Loss-of-significance errors Look at the calculation when x = 0.01: cos(0.01) = (= ) has nine significant digits of accuracy, being off in the tenth digit by two units. Next 1 cos(0.01) = (= e 05) which has only five significant digits, with four digits being lost in the subtraction. To avoid the loss of significant digits, due to the subtraction of nearly equal quantities, we use the Taylor approximation (3.7) for cos(x) about x = 0: cos(x) = 1 x2 2! + x4 4! x6 6! + R 6(x) R 6 (x) = x8 cos(ξ), ξ unknown number between 0 and x. 8!

30 > 2. Error and Computer Arithmetic > Loss-of-significance errors Hence f(x) = 1 ]} {1 x 2 [1 x2 2! + x4 4! x6 6! + R 6(x) = 1 2! x2 4! + x4 6! x6 8! cos(ξ) giving f(0) = 1 2. For x 0.1 x 6 8! cos(ξ) ! Therefore, with this accuracy = f(x) 1 2! x2 4! + x4, x < 0.1 6!

31 > 2. Error and Computer Arithmetic > Loss-of-significance errors Remark When two nearly equal quantities are subtracted, leading significant digits will be lost. In the previous two examples, this was easy to recognize and we found ways to avoid the loss of significance. More often, the loss of significance is subtle and difficult to detect, as in calculating sums (for example, in approximating a function f(x) by a Taylor polynomial). If the value of the sum is relatively small compared to the terms being summed, then there are probably some significant digits of accuracy being lost in the summation process.

32 > 2. Error and Computer Arithmetic > Loss-of-significance errors Example Consider using the Taylor series approximation for e x to evaluate e 5 : e 5 = 1 + ( 5) + ( 5)2 + ( 5)3 + ( 5) ! 2! 3! 4! Degree Term Sum Degree Term Sum E E E E E E E E E E E E Table. Calculation of e 5 = using four-digit decimal arithmetic

33 > 2. Error and Computer Arithmetic > Loss-of-significance errors There are loss-of-significance errors in the calculation of the sum. To avoid the loss of significance is simple in this case: e 5 = 1 e 5 = 1 series for e 5 and form e 5 with a series not involving cancellation of positive and negative terms.

34 > 2. Error and Computer Arithmetic > Noise in function evaluation Consider evaluating a continuous function f for all x [a, b]. The graph is a continuous curve. When evaluating f on a computer using floating-point arithmetic (with rounding or chopping), the errors from arithmetic operations cause the graph to cease being a continuous curve. Let look at on [0, 2]. f(x) = (x 1) 3 = 1 + x(3 + x( 3 + x))

35 > 2. Error and Computer Arithmetic > Noise in function evaluation Figure: f(x) = x 3 3x 2 + 3x 1

36 > 2. Error and Computer Arithmetic > Noise in function evaluation Figure: Detailed graph of f(x) = x 3 3x 2 + 3x 1 near x = 1 Here is a plot of the computed values of f(x) for 81 evenly spaced values of x [ , ]. A rootfinding program might consider f(x) t have a very large number of solutions near 1 based on the many sign changes!!!

37 > 2. Error and Computer Arithmetic > Underflow and overflow errors From the definition of floating-point number, there are upper and lower limits for the magnitudes of the numbers that can be expressed in a floating-point form. Attempts to create numbers that are too small underflow errors: the default option is to set the number to zero and proceed that are too large overflow errors: generally fatal errors on most computers. With the IEEE floating-point format, overflow errors can be carried along as having a value of ± or NaN, depending on the context. Usually, an overflow error is an indication of a more significant problem or error in the program.

38 > 2. Error and Computer Arithmetic > Underflow and overflow errors Example (underflow errors) Consider evaluating f(x) = x 10 for x near 0. With the IEEE single precision arithmetic, the smallest nonzero positive number expressible in normalized floating point arithmetic is So f(x) is set to zero if m = = x 10 < m x < 10 m = < x <

39 > 2. Error and Computer Arithmetic > Underflow and overflow errors Sometimes is possible to eliminate the overflow error by just reformulating the expression being evaluated. Example (overflow errors) Consider evaluating z = x 2 + y 2. If x or y is very large, then x 2 + y 2 might create an overflow error, even though z might be within the floating-point range of the machine. x 1 + ( y 2, x) 0 y x z = ( ) y 1 + x 2, y 0 x y In both cases, the argument of 1 + w 2 has w 1, which will not cause any overflow error (except when z is too large to be expressed in the floating-point format used).

40 > 2. Error and Computer Arithmetic > 2.3 Propagation of Error When doing calculations with numbers that contain an error, the result will be effected by these errors. If x A, y A denote numbers used in a calculation, corresponding to x T, y T, we wish to bound the propagated error E = (x T ωy T ) (x A ωy A ) where ω denotes: +,,,. The first technique used to bound E is interval arithmetic. Suppose we know bounds on x T x A and y T y A. Using these bound and x A ωy A, we look for an interval guaranteed to contain x T ωy T.

41 > 2. Error and Computer Arithmetic > 2.3 Propagation of Error Example Let x A = 3.14 and y A = be correctly rounded from x T and y T, to the number of digits shown. Then x A x T 0.005, y A y T x T y T (4.10) For addition (ω : + ) x A + y A = (4.11) The true value, from (4.10) = x T +y T = (4.12) To bound E, subtract (4.11) from (4.12) to get (x T + y T ) (x A + y A )

42 > 2. Error and Computer Arithmetic > 2.3 Propagation of Error Example For division (ω : ) x A = 3.14 y A The true value, from (4.10), x T y T dividing and rounding to 7 digits: The bound on E x T y T = (4.13) x T y T

43 > 2. Error and Computer Arithmetic > 2.3 Propagation of Error Propagated error in multiplication The relative error in x A y A compared to x T y T is If x T = x A + ɛ, y T = y A + η, then Rel(x A y A ) = x T y T x A y A x T y T Rel(x A y A ) = x T y T x A y A = x T y T (x T ɛ)(y T η) x T y T x T y T = ηx T + ɛy T ɛη = ɛ + η ɛ η x T y T x T y T x T y T = Rel(x A ) + Rel(y A ) Rel(x A )Rel(y A ) If Rel(x A ) 1 and Rel(y A ) 1, then Rel(x A y A ) Rel(x A ) + Rel(y A )

44 > 2. Error and Computer Arithmetic > 2.3 Propagation of Error Propagated error in division The relative error in x A ya compared to x T Rel ( xa y A ) = If x T = x A + ɛ, y T = y A + η, then ( ) x T xa y Rel = T y A x A ya x T y T If Rel(y A ) 1, then = x T η + ɛy T x T (y T η) = y T x T y T = x T y A x A y T x T y A ɛ x T η y T 1 η x T = Rel(x A) Rel(y A ) 1 Rel(y A ) Rel ( xa y A is x A ya x T y T ) Rel(x A ) Rel(y A ) = x T (y T η) (x T ɛ)y T x T (y T η)

45 > 2. Error and Computer Arithmetic > 2.3 Propagation of Error Propagated error in addition and subtraction (x T ± y T ) (x A ± y A ) = (x T x A ) ± (y T y A ) = ɛ ± η Error(x A ± y A ) = Error(x A ) ± Error(y A ) Misleading: we can have much larger Rel(x A ± y A ) for small values of Rel(x A ) and Rel(y A ) (this source of error is connected closely to loss-of-significance errors). Example x T = x A = 13 y T = 168, y A = Rel(x A ) = 0, Rel(y A ) = Error(x A y A ) = Rel(x A y A ) = x T = πx A = y T = 22 7, y A = x T x A = Rel(x A ) = , 2 (y T y A ) = Rel(y A ) = (x T y T ) (x A y A ) = (.0013) = Rel(x A y A ) = 0.28

46 > 2. Error and Computer Arithmetic > 2.3 Propagation of Error Total calculation error With floating-point arithmetic on a computer, x A ωy A involves an additional rounding or chopping error, as in (4.13). Hence the total error in computing x Aˆωy A (the complete operation in computer, involving the propagated error plus the rounding or chopping error): x T ωy T x Aˆωy A = (x T ωy T x A ωy A ) +(x } {{ } A ωy A x Aˆωy A ) } {{ } propagated error error in computing x A ωy A When using IEEE arithmetic with the basic arithmetic operations x Aˆωy A = fl(x A ωy A ) (4.3) = (1 + ɛ)(x A ωy A ) where ɛ as in (4.4)-(4.5), we get x A ωy A x Aˆωy A = ɛ(x A ωy A ) = ɛ x A ωy A x A ˆωy A x A ωy A Hence the process of rounding or chopping introduces a relatively small new error into x Aˆωy A as compared with x A ωy A.

47 > 2. Error and Computer Arithmetic > Propagated error in function evaluation Evaluate f(x) C 1 [a, b] at the approximate value x A instead of x T. Using the mean value theorem f(x T ) f(x A ) = f (c)(x T x A ), c between x T and x A f (x T )(x T x A ) f (x A )(x T x A ) (4.14) and Rel(f(x A )) f (x T ) f(x T ) (x T x A ) = f (x T ) f(x T ) x T Rel(x A ) (4.15)

48 > 2. Error and Computer Arithmetic > Propagated error in function evaluation Example: ideal gas law with R a constant for all gases: P V = nrt R = ɛ, ɛ Let evaluate T assuming P = V = n = 1 : T = 1 R, i.e., evaluate f(x) = 1 x at x = R. Let x T = R, x A = , x T x A For the error we have E = f(x T ) f(x A ) (4.14) E = 1 R ( 1 f (x A ) x T x A x 2 A ) = Hence, the uncertainty ɛ in R relatively small error in the computed 1 R =

49 > 2. Error and Computer Arithmetic > Propagated error in function evaluation Example: evaluate b x, b > 0 Since f (x) = (ln b)b x, by (4.15) b x T b x A (ln b)b x T (x T x A ) Rel(b x A) (ln b)x } {{ T Rel(x } A ) K=conditioning number If the conditioning number K 1 is large in size For example, if then independent of how b x A Rel(b x A ) Rel(x A ) Rel(x A ) = 10 7, K = 10 4 Rel(b x A ) = 10 3 is actually computed.

50 > 2. Error and Computer Arithmetic > 2.4 Summation Let denote the sum S = a 1 + a a n = n a j, where each a j is a floating-point number. It takes (n 1) additions, each of which will probably involve a rounding or chopping error: j=1 S 2 = fl(a 1 + a 2 ) (4.3) = (a 1 + a 2 )(1 + ɛ 2 ) S 3 = fl(a 3 + S 2 ) (4.3) = (a 3 + S 2 )(1 + ɛ 3 ) S 4 = fl(a 4 + S 3 ) (4.3) = (a 4 + S 3 )(1 + ɛ 4 ). S n = fl(a n + S n 1 ) (4.3) = (a n + S n 1 )(1 + ɛ n ) with S n the computed version of S. Each ɛ j satisfies the bounds (4.6)- (4.9), assuming the IEEE arithmetic is used. S S n a 1 (ɛ ɛ n ) a 2 (ɛ ɛ n ) a 3 (ɛ ɛ n ) a 4 (ɛ ɛ n ) a n ɛ n (4.16)

51 > 2. Error and Computer Arithmetic > 2.4 Summation Trying to minimize the error S S N : 1 before summing, arrange the terms a 1, a 2,..., a n so they are increasing in size 2 then summate a 1 a 2 a 3 a n Then the terms in (4.16) with the largest numbers of ɛ j s are multiplied by the smaller values among a j s. Example 1 j decimal fraction round it to 4 significant digits := a j Use a decimal machine with 4 significant digits. SL = adding S from smallest to largest, LS = adding S from largest to smallest.

52 > 2. Error and Computer Arithmetic > 2.4 Summation n True SL Error LS Error Table: Calculating S on a machine using chopping n True SL Error LS Error Table: Calculating S on a machine using rounding

53 > 2. Error and Computer Arithmetic > Rounding versus chopping A more important difference in the errors in the previous Tables: between rounding and chopping. Rounding a far smaller error in the calculated sum than does chopping. Let go back to the first term in (4.16): T a 1 (ɛ 1 + ɛ ɛ n ) (4.17) (1) Assuming that the previous example used rounding with four-digit decimal machine, we know that all ɛ j satisfy ɛ j (4.18) Treating rounding errors as being random, then the positive and negative values of the ɛ j s will tend to cancel and the sum T will be nearly zero. Moreover, from probability theory: T n a 1 hence T is proportional to n and is small until n becomes quite large. Similarly for the total error in (4.16).

54 > 2. Error and Computer Arithmetic > Rounding versus chopping (2) For chopping with the four-digit decimal machine, (4.18) is replaced by and all errors have one sign ɛ j 0 Again, the errors will be random in this interval, with the average and (4.17) will likely be a 1 (n 1) ( ) hence T is proportional to n, which increases more rapidly than n. Thus the error (4.17) and (4.16) will grow more rapidly when chopping is used rather than rounding.

55 > 2. Error and Computer Arithmetic > Rounding versus chopping Example (difference between rounding and chopping on summation) Consider S = n j=1 in single precision accuracy of six decimal digits. Errors occur both in calculation of 1 j 1 j and in the summation process. n True Rounding error Chopping Error E E E E E E E E E E-5 Table: Calculation of S: rounding versus chopping The true values were calculated using double precision arithmetic to evaluate S; all sums were performed from the smallest to the largest.

56 > 2. Error and Computer Arithmetic > A loop error Suppose we wish to calculate: x = a + jh, (4.19) for j = 0, 1, 2,..., n, for given h > 0, which is used later to evaluate a function f(x). Question Should we compute x using (4.19) or by using x = x + h (4.20) in the loop, having initially set x = a before beginning the loop? Remark: These are mathematically equivalent ways to compute x, but they are usually not computationally equivalent. The difficulty arises when h does not have a finite binary expansion that can be stored in the given floating-point significand; for example h = 0.1

57 > 2. Error and Computer Arithmetic > A loop error The computation (4.19) x = a + jh will involve two arithmetic operations, hence only two chopping or rounding errors, for each value of x. In contrast, the repeated use of (4.20) x = x + h will involve a succession of j additions, for the x of (4.19). As x increases in size, the use of (4.20) involves a larger number of rounding or chopping errors, leading to a different quantity than in (4.19). Usually (4.19) is the preferred way to evaluate x Example Compute x in the two ways, with a = 0, h = 0.1 and verify with the true value computed using a double precision computation.

58 > 2. Error and Computer Arithmetic > A loop error j x Error using (4.19) Error using (4.20) E E E E E E E E E E E E E E E E E E E E-6 Table: Evaluation of x = j h, h = 0.1

59 > 2. Error and Computer Arithmetic > Calculation of inner products Definition (inner product) A sum of the form S = a 1 b 1 + a 2 b a n b n = is called a dot product or inner product. n a j b j (4.21) (1) If we calculate S in single precision: a single precision rounding error for each multiplication and each addition 2n 1 single precision rounding errors to calculate S. The consequences of these errors can be analyzed as for (4.16) and derive an optimal strategy of calculating (4.21). j=1

60 > 2. Error and Computer Arithmetic > Calculation of inner products (2) Using double precision: 1 convert each a j and b j to double precision by extending their significands with zeros 2 multiply in double precision 3 sum in double precision 4 round to single precision to obtain the calculated value of S For machines with IEEE arithmetic, this is a simple and rapid procedure to obtain more accurate inner products in single precision. There is no increase in storage space for the arrays A = [a 1, a 2,..., a n ] and B. The accuracy is improved since there is only one single precision rounding error, regardless of the size n.

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