《数字信号处理》教学参考资料（Numerical Recipes in C，The Art of Scientific Computing Second Edition）Chapter 03.4 Interpolation and Extrapolation 3.4 How to Search an Ordered Table

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3.4 How to Search an Ordered Table 117 3.4 How to Search an Ordered Table Suppose that you have decided to use some particular interpolation scheme, such as fourth-order polynomial interpolation,to compute a function f(z)from a set of tabulated x;'s and f;'s.Then you will need a fast way of finding your place in the table of zi's,given some particular value z at which the function evaluation is desired.This problem is not properly one of numerical analysis,but it occurs so often in practice that it would be negligent of us to ignore it. Formally,the problem is this:Given an array ofabscissas xx[j],j=1,2,...n, with the elements either monotonically increasing or monotonically decreasing,and given a number x,find an integer j such that x lies between xx[j]and xx [j+1]. For this task,let us define fictitious array elements xx[o]and xx[n+1]equal to plus or minus infinity (in whichever order is consistent with the monotonicity of the 含 table).Then j will always be between 0 and n,inclusive;a value of 0 indicates 'off-scale"at one end of the table.n indicates off-scale at the other end. In most cases,when all is said and done,it is hard to do better than bisection, RECIPES I which will find the right place in the table in about log2n tries.We already did use 2 bisection in the spline evaluation routine splint of the preceding section,so you might glance back at that.Standing by itself,a bisection routine looks like this: void locate(float xx[],unsigned long n,float x,unsigned long *j) Given an array xx[1..n],and given a value x,returns a value j such that x is between xx[j] and xx[j+1].xx must be monotonic,either increasing or decreasing.j=0 or j=n is returned to indicate that x is out of range. 、9 OF SCIENTIFIC unsigned long ju,jm,jl; int ascnd; to dir j1=0; Initialize lower ju=n+1; and upper limits. COMPUTING ascnd=(xx[n]>xx[1]); h11e(ju-j1>1)( If we are not yet done, jm=(ju+j1)>1; compute a midpoint, (ISBN if (x >xx[im]=ascnd) jl=jm; and replace either the lower limit v@cambri 10621 else ju=jm; or the upper limit,as appropriate Repeat until the test condition is satisfied. 1988-1992 by Numerical Recipes 43108 1f(x=xx[1])*j=1; Then set the output else if(x =xx[n])*j=n-1; else *j=jl; and return. (outside North Software. A unit-offset array xx is assumed.To use locate with a zero-offset array, Ame ying of remember to subtract 1 from the address of xx,and also from the returned value j. Search with Correlated Values Sometimes you will be in the situation of searching a large table many times, and with nearly identical abscissas on consecutive searches.For example,you may be generating a function that is used on the right-hand side of a differential equation:Most differential-equation integrators,as we shall see in Chapter 16,call

3.4 How to Search an Ordered Table 117 Permission is granted for internet users to make one paper copy for their own personal use. Further reproduction, or any copyin Copyright (C) 1988-1992 by Cambridge University Press. Programs Copyright (C) 1988-1992 by Numerical Recipes Software. Sample page from NUMERICAL RECIPES IN C: THE ART OF SCIENTIFIC COMPUTING (ISBN 0-521-43108-5) g of machinereadable files (including this one) to any server computer, is strictly prohibited. To order Numerical Recipes books or CDROMs, visit website http://www.nr.com or call 1-800-872-7423 (North America only), or send email to directcustserv@cambridge.org (outside North America). 3.4 How to Search an Ordered Table Suppose that you have decided to use some particular interpolation scheme, such as fourth-order polynomial interpolation, to compute a function f(x) from a set of tabulated xi’s and fi’s. Then you will need a fast way of finding your place in the table of xi’s, given some particular value x at which the function evaluation is desired. This problem is not properly one of numerical analysis, but it occurs so often in practice that it would be negligent of us to ignore it. Formally, the problem is this: Given an array of abscissas xx[j], j=1, 2, ...,n, with the elements either monotonically increasing or monotonically decreasing, and given a number x, find an integer j such that x lies between xx[j] and xx[j+1]. For this task, let us define fictitious array elements xx[0] and xx[n+1] equal to plus or minus infinity (in whichever order is consistent with the monotonicity of the table). Then j will always be between 0 and n, inclusive; a value of 0 indicates “off-scale” at one end of the table, n indicates off-scale at the other end. In most cases, when all is said and done, it is hard to do better than bisection, which will find the right place in the table in about log 2n tries. We already did use bisection in the spline evaluation routine splint of the preceding section, so you might glance back at that. Standing by itself, a bisection routine looks like this: void locate(float xx[], unsigned long n, float x, unsigned long *j) Given an array xx[1..n], and given a value x, returns a value j such that x is between xx[j] and xx[j+1]. xx must be monotonic, either increasing or decreasing. j=0 or j=n is returned to indicate that x is out of range. { unsigned long ju,jm,jl; int ascnd; jl=0; Initialize lower ju=n+1; and upper limits. ascnd=(xx[n] >= xx[1]); while (ju-jl > 1) { If we are not yet done, jm=(ju+jl) >> 1; compute a midpoint, if (x >= xx[jm] == ascnd) jl=jm; and replace either the lower limit else ju=jm; or the upper limit, as appropriate. } Repeat until the test condition is satisfied. if (x == xx[1]) *j=1; Then set the output else if(x == xx[n]) *j=n-1; else *j=jl; } and return. A unit-offset array xx is assumed. To use locate with a zero-offset array, remember to subtract 1 from the address of xx, and also from the returned value j. Search with Correlated Values Sometimes you will be in the situation of searching a large table many times, and with nearly identical abscissas on consecutive searches. For example, you may be generating a function that is used on the right-hand side of a differential equation: Most differential-equation integrators, as we shall see in Chapter 16, call

118 Chapter 3. Interpolation and Extrapolation 1 32 64 (a) 51 32 8 hunt phase fO 83 71014 22 /38 granted for 19881992 (b) bisection phase 1.200 Figure 3.4.1.(a)The routine locate finds a table entry by bisection.Shown here is the sequence of steps that converge to element 51 in a table of length 64.(b)The routine hunt searches from a previous known position in the table by increasing steps,then converges by bisection.Shown here is a from NUMERICAL RECIPESI particularly unfavorable example,converging to element 32 from element 7.A favorable example would be convergence to an element near 7,such as 9,which would require just three"hops." (Nort server to make University Press. 令 for right-hand side evaluations at points that hop back and forth a bit,but whose THE trend moves slowly in the direction of the integration. America computer, In such cases it is wasteful to do a full bisection,ab initio,on each call.The ART following routine instead starts with a guessed position in the table.It first"hunts," 9 Program either up or down,in increments of 1,then 2,then 4,etc.,until the desired value is bracketed.Second.it then bisects in the bracketed interval.At worst.this routine is about a factor of 2 slower than locate above(if the hunt phase expands to include the whole table).At best,it can be a factor of logn faster than locate,if the desired point is usually quite close to the input guess.Figure 3.4.I compares the two routines. to dir void hunt(float xx[],unsigned long n,float x,unsigned long *jlo) OF SCIENTIFIC COMPUTING(ISBN Given an array xx[1..n],and given a value x,returns a value jlo such that x is between 1988-19920 xx[jlo]and xx[jlo+1].xx[1..n]must be monotonic,either increasing or decreasing. jlo=0 or jlo=n is returned to indicate that x is out of range.jlo on input is taken as the initial guess for jlo on output. 10-621 unsigned long im,jhi,inc; Numerical Recipes -43108 int ascnd; ascnd=(xx[n]>xx[1]); True if ascending order of table,false otherwise. if(*j1on)( Input guess not useful.Go immediately to bisec- (outside *j1o=0; tion. jhisn+1: Software. else inc=1; Set the hunting increment ying of if (x >xx[*jlo]==ascnd){Hunt up if (*ilo =n)return; jhi=(*j1o)+1; while (x >xx[jhi]=ascnd){ Not done hunting. *jlo=ihi: inc +inc; so double the increment jhi=(*jlo)+inc; if (jhi n){ Done hunting.since off end of table jhi=n+1; break; Try again

118 Chapter 3. Interpolation and Extrapolation Permission is granted for internet users to make one paper copy for their own personal use. Further reproduction, or any copyin Copyright (C) 1988-1992 by Cambridge University Press. Programs Copyright (C) 1988-1992 by Numerical Recipes Software. Sample page from NUMERICAL RECIPES IN C: THE ART OF SCIENTIFIC COMPUTING (ISBN 0-521-43108-5) g of machinereadable files (including this one) to any server computer, is strictly prohibited. To order Numerical Recipes books or CDROMs, visit website http://www.nr.com or call 1-800-872-7423 (North America only), or send email to directcustserv@cambridge.org (outside North America). hunt phase bisection phase 1 7 10 8 14 22 32 38 1 32 (a) (b) 51 64 Figure 3.4.1. (a) The routine locate finds a table entry by bisection. Shown here is the sequence of steps that converge to element 51 in a table of length 64. (b) The routine hunt searches from a previous known position in the table by increasing steps, then converges by bisection. Shown here is a particularly unfavorable example, converging to element 32 from element 7. A favorable example would be convergence to an element near 7, such as 9, which would require just three “hops.” for right-hand side evaluations at points that hop back and forth a bit, but whose trend moves slowly in the direction of the integration. In such cases it is wasteful to do a full bisection, ab initio, on each call. The following routine instead starts with a guessed position in the table. It first “hunts,” either up or down, in increments of 1, then 2, then 4, etc., until the desired value is bracketed. Second, it then bisects in the bracketed interval. At worst, this routine is about a factor of 2 slower than locate above (if the hunt phase expands to include the whole table). At best, it can be a factor of log2n faster than locate, if the desired point is usually quite close to the input guess. Figure 3.4.1 compares the two routines. void hunt(float xx[], unsigned long n, float x, unsigned long *jlo) Given an array xx[1..n], and given a value x, returns a value jlo such that x is between xx[jlo] and xx[jlo+1]. xx[1..n] must be monotonic, either increasing or decreasing. jlo=0 or jlo=n is returned to indicate that x is out of range. jlo on input is taken as the initial guess for jlo on output. { unsigned long jm,jhi,inc; int ascnd; ascnd=(xx[n] >= xx[1]); True if ascending order of table, false otherwise. if (*jlo n) { Input guess not useful. Go immediately to bisec- *jlo=0; tion. jhi=n+1; } else { inc=1; Set the hunting increment. if (x >= xx[*jlo] == ascnd) { Hunt up: if (*jlo == n) return; jhi=(*jlo)+1; while (x >= xx[jhi] == ascnd) { Not done hunting, *jlo=jhi; inc += inc; so double the increment jhi=(*jlo)+inc; if (jhi > n) { Done hunting, since off end of table. jhi=n+1; break; } Try again

3.4 How to Search an Ordered Table 119 Permission is granted for internet users to make one paper copy for their own personal use. Further reproduction, or any copyin Copyright (C) 1988-1992 by Cambridge University Press. Programs Copyright (C) 1988-1992 by Numerical Recipes Software. Sample page from NUMERICAL RECIPES IN C: THE ART OF SCIENTIFIC COMPUTING (ISBN 0-521-43108-5) g of machinereadable files (including this one) to any server computer, is strictly prohibited. To order Numerical Recipes books or CDROMs, visit website http://www.nr.com or call 1-800-872-7423 (North America only), or send email to directcustserv@cambridge.org (outside North America). } Done hunting, value bracketed. } else { Hunt down: if (*jlo == 1) { *jlo=0; return; } jhi=(*jlo)--; while (x = jhi) { Done hunting, since off end of table. *jlo=0; break; } else *jlo=jhi-inc; } and try again. } Done hunting, value bracketed. } Hunt is done, so begin the final bisection phase: while (jhi-(*jlo) != 1) { jm=(jhi+(*jlo)) >> 1; if (x >= xx[jm] == ascnd) *jlo=jm; else jhi=jm; } if (x == xx[n]) *jlo=n-1; if (x == xx[1]) *jlo=1; } If your array xx is zero-offset, read the comment following locate, above. After the Hunt The problem: Routines locate and hunt return an index j such that your desired value lies between table entries xx[j] and xx[j+1], where xx[1..n] is the full length of the table. But, to obtain an m-point interpolated value using a routine like polint (§3.1) or ratint (§3.2), you need to supply much shorter xx and yy arrays, of length m. How do you make the connection? The solution: Calculate k = IMIN(IMAX(j-(m-1)/2,1),n+1-m) (The macros IMIN and IMAX give the minimum and maximum of two integer arguments; see §1.2 and Appendix B.) This expression produces the index of the leftmost member of an m-point set of points centered (insofar as possible) between j and j+1, but bounded by 1 at the left and n at the right. C then lets you call the interpolation routine with array addresses offset by k, e.g., polint(&xx[k-1],&yy[k-1],m,... ) CITED REFERENCES AND FURTHER READING: Knuth, D.E. 1973, Sorting and Searching, vol. 3 of The Art of Computer Programming (Reading, MA: Addison-Wesley), §6.2.1

120 Chapter 3.Interpolation and Extrapolation 3.5 Coefficients of the Interpolating Polynomial Occasionally you may wish to know not the value of the interpolating polynomial that passes through a (small!)number of points,but the coefficients of that poly- nomial.A valid use of the coefficients might be,for example,to compute simultaneous interpolated values of the function and of several of its derivatives (see S5.3),or to convolve a segment of the tabulated function with some other function, where the moments of that other function (i.e..its convolution with powers of x) are known analytically. However,please be certain that the coefficients are what you need.Generally the coefficients of the interpolating polynomial can be determined much less accurately than its value at a desired abscissa.Therefore it is not a good idea to determine the coefficients only for use in calculating interpolating values.Values thus calculated will not pass exactly through the tabulated points,for example,while values computed by the routines in 83.1-83.3 will pass exactly through such points. Also,you should not mistake the interpolating polynomial(and its coefficients) for its cousin,the best fit polynomial through a data set.Fitting is a smoothing process,since the number of fitted coefficients is typically much less than the 9 number of data points.Therefore,fitted coefficients can be accurately and stably determined even in the presence of statistical errors in the tabulated values.(See $14.8.)Interpolation,where the number of coefficients and number of tabulated points are equal,takes the tabulated values as perfect.If they in fact contain statistical errors,these can be magnified into oscillations of the interpolating polynomial in 草色%A5 between the tabulated points. As before,we take the tabulated points to be yi=y(i).If the interpolating OF SCIENTIFIC( polynomial is written as 61 y=co+ci+c22+...+CNzN 3.5.1) then the ci's are required to satisfy the linear equation 哈 10.621 1 To C07 yo Numerica 1 x 1 431 (3.5.2) (outside Recipes TN CN -yN North This is a Vandermonde matrix,as described in $2.8.One could in principle solve equation(3.5.2)by standard techniques for linear equations generally(82.3);however the special method that was derived in 82.8 is more efficient by a large factor,of order N,so it is much better. Remember that Vandermonde systems can be quite ill-conditioned.In such a case,no numerical method is going to give a very accurate answer.Such cases do not,please note,imply any difficulty in finding interpolated values by the methods of 83.1,but only difficulty in finding coefficients. Like the routine in $2.8,the following is due to G.B.Rybicki.Note that the arrays are all assumed to be zero-offset

120 Chapter 3. Interpolation and Extrapolation Permission is granted for internet users to make one paper copy for their own personal use. Further reproduction, or any copyin Copyright (C) 1988-1992 by Cambridge University Press. Programs Copyright (C) 1988-1992 by Numerical Recipes Software. Sample page from NUMERICAL RECIPES IN C: THE ART OF SCIENTIFIC COMPUTING (ISBN 0-521-43108-5) g of machinereadable files (including this one) to any server computer, is strictly prohibited. To order Numerical Recipes books or CDROMs, visit website http://www.nr.com or call 1-800-872-7423 (North America only), or send email to directcustserv@cambridge.org (outside North America). 3.5 Coefficients of the Interpolating Polynomial Occasionally you may wish to know not the value of the interpolating polynomial that passes through a (small!) number of points, but the coefficients of that polynomial. A valid use of the coefficients might be, for example, to compute simultaneous interpolated values of the function and of several of its derivatives (see §5.3), or to convolve a segment of the tabulated function with some other function, where the moments of that other function (i.e., its convolution with powers of x) are known analytically. However, please be certain that the coefficients are what you need. Generally the coefficients of the interpolating polynomial can be determined much less accurately than its value at a desired abscissa. Therefore it is not a good idea to determine the coefficients only for use in calculating interpolating values. Values thus calculated will not pass exactly through the tabulated points,for example, while values computed by the routines in §3.1–§3.3 will pass exactly through such points. Also, you should not mistake the interpolating polynomial (and its coefficients) for its cousin, the best fit polynomial through a data set. Fitting is a smoothing process, since the number of fitted coefficients is typically much less than the number of data points. Therefore, fitted coefficients can be accurately and stably determined even in the presence of statistical errors in the tabulated values. (See §14.8.) Interpolation, where the number of coefficients and number of tabulated points are equal, takes the tabulated values as perfect. If they in fact contain statistical errors, these can be magnified into oscillations of the interpolating polynomial in between the tabulated points. As before, we take the tabulated points to be yi ≡ y(xi). If the interpolating polynomial is written as y = c0 + c1x + c2x2 + ··· + cN xN (3.5.1) then the ci’s are required to satisfy the linear equation      1 x0 x2 0 ··· xN 0 1 x1 x2 1 ··· xN 1 . . . . . . . . . . . . 1 xN x2 N ··· xN N      ·      c0 c1 . . . cN      =      y0 y1 . . . yN      (3.5.2) This is a Vandermonde matrix, as described in §2.8. One could in principle solve equation (3.5.2) by standard techniques for linear equations generally (§2.3); however the special method that was derived in §2.8 is more efficient by a large factor, of order N, so it is much better. Remember that Vandermonde systems can be quite ill-conditioned. In such a case, no numerical method is going to give a very accurate answer. Such cases do not, please note, imply any difficulty in finding interpolated values by the methods of §3.1, but only difficulty in finding coefficients. Like the routine in §2.8, the following is due to G.B. Rybicki. Note that the arrays are all assumed to be zero-offset

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