LAPACK  3.11.0
LAPACK: Linear Algebra PACKage
zlaic1.f
1 *> \brief \b ZLAIC1 applies one step of incremental condition estimation.
2 *
3 * =========== DOCUMENTATION ===========
4 *
5 * Online html documentation available at
6 * http://www.netlib.org/lapack/explore-html/
7 *
8 *> \htmlonly
9 *> Download ZLAIC1 + dependencies
10 *> <a href="http://www.netlib.org/cgi-bin/netlibfiles.tgz?format=tgz&filename=/lapack/lapack_routine/zlaic1.f">
11 *> [TGZ]</a>
12 *> <a href="http://www.netlib.org/cgi-bin/netlibfiles.zip?format=zip&filename=/lapack/lapack_routine/zlaic1.f">
13 *> [ZIP]</a>
14 *> <a href="http://www.netlib.org/cgi-bin/netlibfiles.txt?format=txt&filename=/lapack/lapack_routine/zlaic1.f">
15 *> [TXT]</a>
16 *> \endhtmlonly
17 *
18 * Definition:
19 * ===========
20 *
21 * SUBROUTINE ZLAIC1( JOB, J, X, SEST, W, GAMMA, SESTPR, S, C )
22 *
23 * .. Scalar Arguments ..
24 * INTEGER J, JOB
25 * DOUBLE PRECISION SEST, SESTPR
26 * COMPLEX*16 C, GAMMA, S
27 * ..
28 * .. Array Arguments ..
29 * COMPLEX*16 W( J ), X( J )
30 * ..
31 *
32 *
33 *> \par Purpose:
34 * =============
35 *>
36 *> \verbatim
37 *>
38 *> ZLAIC1 applies one step of incremental condition estimation in
39 *> its simplest version:
40 *>
41 *> Let x, twonorm(x) = 1, be an approximate singular vector of an j-by-j
42 *> lower triangular matrix L, such that
43 *> twonorm(L*x) = sest
44 *> Then ZLAIC1 computes sestpr, s, c such that
45 *> the vector
46 *> [ s*x ]
47 *> xhat = [ c ]
48 *> is an approximate singular vector of
49 *> [ L 0 ]
50 *> Lhat = [ w**H gamma ]
51 *> in the sense that
52 *> twonorm(Lhat*xhat) = sestpr.
53 *>
54 *> Depending on JOB, an estimate for the largest or smallest singular
55 *> value is computed.
56 *>
57 *> Note that [s c]**H and sestpr**2 is an eigenpair of the system
58 *>
59 *> diag(sest*sest, 0) + [alpha gamma] * [ conjg(alpha) ]
60 *> [ conjg(gamma) ]
61 *>
62 *> where alpha = x**H * w.
63 *> \endverbatim
64 *
65 * Arguments:
66 * ==========
67 *
68 *> \param[in] JOB
69 *> \verbatim
70 *> JOB is INTEGER
71 *> = 1: an estimate for the largest singular value is computed.
72 *> = 2: an estimate for the smallest singular value is computed.
73 *> \endverbatim
74 *>
75 *> \param[in] J
76 *> \verbatim
77 *> J is INTEGER
78 *> Length of X and W
79 *> \endverbatim
80 *>
81 *> \param[in] X
82 *> \verbatim
83 *> X is COMPLEX*16 array, dimension (J)
84 *> The j-vector x.
85 *> \endverbatim
86 *>
87 *> \param[in] SEST
88 *> \verbatim
89 *> SEST is DOUBLE PRECISION
90 *> Estimated singular value of j by j matrix L
91 *> \endverbatim
92 *>
93 *> \param[in] W
94 *> \verbatim
95 *> W is COMPLEX*16 array, dimension (J)
96 *> The j-vector w.
97 *> \endverbatim
98 *>
99 *> \param[in] GAMMA
100 *> \verbatim
101 *> GAMMA is COMPLEX*16
102 *> The diagonal element gamma.
103 *> \endverbatim
104 *>
105 *> \param[out] SESTPR
106 *> \verbatim
107 *> SESTPR is DOUBLE PRECISION
108 *> Estimated singular value of (j+1) by (j+1) matrix Lhat.
109 *> \endverbatim
110 *>
111 *> \param[out] S
112 *> \verbatim
113 *> S is COMPLEX*16
114 *> Sine needed in forming xhat.
115 *> \endverbatim
116 *>
117 *> \param[out] C
118 *> \verbatim
119 *> C is COMPLEX*16
120 *> Cosine needed in forming xhat.
121 *> \endverbatim
122 *
123 * Authors:
124 * ========
125 *
126 *> \author Univ. of Tennessee
127 *> \author Univ. of California Berkeley
128 *> \author Univ. of Colorado Denver
129 *> \author NAG Ltd.
130 *
131 *> \ingroup laic1
132 *
133 * =====================================================================
134  SUBROUTINE zlaic1( JOB, J, X, SEST, W, GAMMA, SESTPR, S, C )
135 *
136 * -- LAPACK auxiliary routine --
137 * -- LAPACK is a software package provided by Univ. of Tennessee, --
138 * -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
139 *
140 * .. Scalar Arguments ..
141  INTEGER J, JOB
142  DOUBLE PRECISION SEST, SESTPR
143  COMPLEX*16 C, GAMMA, S
144 * ..
145 * .. Array Arguments ..
146  COMPLEX*16 W( j ), X( j )
147 * ..
148 *
149 * =====================================================================
150 *
151 * .. Parameters ..
152  DOUBLE PRECISION ZERO, ONE, TWO
153  parameter( zero = 0.0d0, one = 1.0d0, two = 2.0d0 )
154  DOUBLE PRECISION HALF, FOUR
155  parameter( half = 0.5d0, four = 4.0d0 )
156 * ..
157 * .. Local Scalars ..
158  DOUBLE PRECISION ABSALP, ABSEST, ABSGAM, B, EPS, NORMA, S1, S2,
159  $ scl, t, test, tmp, zeta1, zeta2
160  COMPLEX*16 ALPHA, COSINE, SINE
161 * ..
162 * .. Intrinsic Functions ..
163  INTRINSIC abs, dconjg, max, sqrt
164 * ..
165 * .. External Functions ..
166  DOUBLE PRECISION DLAMCH
167  COMPLEX*16 ZDOTC
168  EXTERNAL dlamch, zdotc
169 * ..
170 * .. Executable Statements ..
171 *
172  eps = dlamch( 'Epsilon' )
173  alpha = zdotc( j, x, 1, w, 1 )
174 *
175  absalp = abs( alpha )
176  absgam = abs( gamma )
177  absest = abs( sest )
178 *
179  IF( job.EQ.1 ) THEN
180 *
181 * Estimating largest singular value
182 *
183 * special cases
184 *
185  IF( sest.EQ.zero ) THEN
186  s1 = max( absgam, absalp )
187  IF( s1.EQ.zero ) THEN
188  s = zero
189  c = one
190  sestpr = zero
191  ELSE
192  s = alpha / s1
193  c = gamma / s1
194  tmp = dble( sqrt( s*dconjg( s )+c*dconjg( c ) ) )
195  s = s / tmp
196  c = c / tmp
197  sestpr = s1*tmp
198  END IF
199  RETURN
200  ELSE IF( absgam.LE.eps*absest ) THEN
201  s = one
202  c = zero
203  tmp = max( absest, absalp )
204  s1 = absest / tmp
205  s2 = absalp / tmp
206  sestpr = tmp*sqrt( s1*s1+s2*s2 )
207  RETURN
208  ELSE IF( absalp.LE.eps*absest ) THEN
209  s1 = absgam
210  s2 = absest
211  IF( s1.LE.s2 ) THEN
212  s = one
213  c = zero
214  sestpr = s2
215  ELSE
216  s = zero
217  c = one
218  sestpr = s1
219  END IF
220  RETURN
221  ELSE IF( absest.LE.eps*absalp .OR. absest.LE.eps*absgam ) THEN
222  s1 = absgam
223  s2 = absalp
224  IF( s1.LE.s2 ) THEN
225  tmp = s1 / s2
226  scl = sqrt( one+tmp*tmp )
227  sestpr = s2*scl
228  s = ( alpha / s2 ) / scl
229  c = ( gamma / s2 ) / scl
230  ELSE
231  tmp = s2 / s1
232  scl = sqrt( one+tmp*tmp )
233  sestpr = s1*scl
234  s = ( alpha / s1 ) / scl
235  c = ( gamma / s1 ) / scl
236  END IF
237  RETURN
238  ELSE
239 *
240 * normal case
241 *
242  zeta1 = absalp / absest
243  zeta2 = absgam / absest
244 *
245  b = ( one-zeta1*zeta1-zeta2*zeta2 )*half
246  c = zeta1*zeta1
247  IF( b.GT.zero ) THEN
248  t = dble( c / ( b+sqrt( b*b+c ) ) )
249  ELSE
250  t = dble( sqrt( b*b+c ) - b )
251  END IF
252 *
253  sine = -( alpha / absest ) / t
254  cosine = -( gamma / absest ) / ( one+t )
255  tmp = dble( sqrt( sine * dconjg( sine )
256  $ + cosine * dconjg( cosine ) ) )
257 
258  s = sine / tmp
259  c = cosine / tmp
260  sestpr = sqrt( t+one )*absest
261  RETURN
262  END IF
263 *
264  ELSE IF( job.EQ.2 ) THEN
265 *
266 * Estimating smallest singular value
267 *
268 * special cases
269 *
270  IF( sest.EQ.zero ) THEN
271  sestpr = zero
272  IF( max( absgam, absalp ).EQ.zero ) THEN
273  sine = one
274  cosine = zero
275  ELSE
276  sine = -dconjg( gamma )
277  cosine = dconjg( alpha )
278  END IF
279  s1 = max( abs( sine ), abs( cosine ) )
280  s = sine / s1
281  c = cosine / s1
282  tmp = dble( sqrt( s*dconjg( s )+c*dconjg( c ) ) )
283  s = s / tmp
284  c = c / tmp
285  RETURN
286  ELSE IF( absgam.LE.eps*absest ) THEN
287  s = zero
288  c = one
289  sestpr = absgam
290  RETURN
291  ELSE IF( absalp.LE.eps*absest ) THEN
292  s1 = absgam
293  s2 = absest
294  IF( s1.LE.s2 ) THEN
295  s = zero
296  c = one
297  sestpr = s1
298  ELSE
299  s = one
300  c = zero
301  sestpr = s2
302  END IF
303  RETURN
304  ELSE IF( absest.LE.eps*absalp .OR. absest.LE.eps*absgam ) THEN
305  s1 = absgam
306  s2 = absalp
307  IF( s1.LE.s2 ) THEN
308  tmp = s1 / s2
309  scl = sqrt( one+tmp*tmp )
310  sestpr = absest*( tmp / scl )
311  s = -( dconjg( gamma ) / s2 ) / scl
312  c = ( dconjg( alpha ) / s2 ) / scl
313  ELSE
314  tmp = s2 / s1
315  scl = sqrt( one+tmp*tmp )
316  sestpr = absest / scl
317  s = -( dconjg( gamma ) / s1 ) / scl
318  c = ( dconjg( alpha ) / s1 ) / scl
319  END IF
320  RETURN
321  ELSE
322 *
323 * normal case
324 *
325  zeta1 = absalp / absest
326  zeta2 = absgam / absest
327 *
328  norma = max( one+zeta1*zeta1+zeta1*zeta2,
329  $ zeta1*zeta2+zeta2*zeta2 )
330 *
331 * See if root is closer to zero or to ONE
332 *
333  test = one + two*( zeta1-zeta2 )*( zeta1+zeta2 )
334  IF( test.GE.zero ) THEN
335 *
336 * root is close to zero, compute directly
337 *
338  b = ( zeta1*zeta1+zeta2*zeta2+one )*half
339  c = zeta2*zeta2
340  t = dble( c / ( b+sqrt( abs( b*b-c ) ) ) )
341  sine = ( alpha / absest ) / ( one-t )
342  cosine = -( gamma / absest ) / t
343  sestpr = sqrt( t+four*eps*eps*norma )*absest
344  ELSE
345 *
346 * root is closer to ONE, shift by that amount
347 *
348  b = ( zeta2*zeta2+zeta1*zeta1-one )*half
349  c = zeta1*zeta1
350  IF( b.GE.zero ) THEN
351  t = dble( -c / ( b+sqrt( b*b+c ) ) )
352  ELSE
353  t = dble( b - sqrt( b*b+c ) )
354  END IF
355  sine = -( alpha / absest ) / t
356  cosine = -( gamma / absest ) / ( one+t )
357  sestpr = sqrt( one+t+four*eps*eps*norma )*absest
358  END IF
359  tmp = dble( sqrt( sine * dconjg( sine )
360  $ + cosine * dconjg( cosine ) ) )
361  s = sine / tmp
362  c = cosine / tmp
363  RETURN
364 *
365  END IF
366  END IF
367  RETURN
368 *
369 * End of ZLAIC1
370 *
371  END
subroutine zlaic1(JOB, J, X, SEST, W, GAMMA, SESTPR, S, C)
ZLAIC1 applies one step of incremental condition estimation.
Definition: zlaic1.f:135