Date: 2022-11-02T11:46:32+00:00

Actual source code: filtlan.c
slepc-3.18.1 2022-11-02
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      1: /*
      2:    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
      3:    SLEPc - Scalable Library for Eigenvalue Problem Computations
      4:    Copyright (c) 2002-, Universitat Politecnica de Valencia, Spain

      6:    This file is part of SLEPc.
      7:    SLEPc is distributed under a 2-clause BSD license (see LICENSE).
      8:    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
      9: */
     10: /*
     11:    This file is an adaptation of several subroutines from FILTLAN, the
     12:    Filtered Lanczos Package, authored by Haw-ren Fang and Yousef Saad.

     14:    More information at:
     15:    https://www-users.cs.umn.edu/~saad/software/filtlan

     17:    References:

     19:        [1] H. Fang and Y. Saad, "A filtered Lanczos procedure for extreme and interior
     20:            eigenvalue problems", SIAM J. Sci. Comput. 34(4):A2220-A2246, 2012.
     21: */

     23: #include <slepc/private/stimpl.h>
     24: #include "filter.h"

     26: static PetscErrorCode FILTLAN_FilteredConjugateResidualPolynomial(PetscReal*,PetscReal*,PetscInt,PetscReal*,PetscInt,PetscReal*,PetscInt);
     27: static PetscReal FILTLAN_PiecewisePolynomialEvaluationInChebyshevBasis(PetscReal*,PetscInt,PetscReal*,PetscInt,PetscReal);
     28: static PetscErrorCode FILTLAN_ExpandNewtonPolynomialInChebyshevBasis(PetscInt,PetscReal,PetscReal,PetscReal*,PetscReal*,PetscReal*,PetscReal*);

     30: /* ////////////////////////////////////////////////////////////////////////////
     31:    //    Newton - Hermite Polynomial Interpolation
     32:    //////////////////////////////////////////////////////////////////////////// */

     34: /*
     35:    FILTLAN function NewtonPolynomial

     37:    build P(z) by Newton's divided differences in the form
     38:        P(z) = a(1) + a(2)*(z-x(1)) + a(3)*(z-x(1))*(z-x(2)) + ... + a(n)*(z-x(1))*...*(z-x(n-1)),
     39:    such that P(x(i)) = y(i) for i=1,...,n, where
     40:        x,y are input vectors of length n, and a is the output vector of length n
     41:    if x(i)==x(j) for some i!=j, then it is assumed that the derivative of P(z) is to be zero at x(i),
     42:        and the Hermite polynomial interpolation is applied
     43:    in general, if there are k x(i)'s with the same value x0, then
     44:        the j-th order derivative of P(z) is zero at z=x0 for j=1,...,k-1
     45: */
     46: static inline PetscErrorCode FILTLAN_NewtonPolynomial(PetscInt n,PetscReal *x,PetscReal *y,PetscReal *sa,PetscReal *sf)
     47: {
     48:   PetscReal      d,*sx=x,*sy=y;
     49:   PetscInt       j,k;

     51:   PetscArraycpy(sf,sy,n);

     53:   /* apply Newton's finite difference method */
     54:   sa[0] = sf[0];
     55:   for (j=1;j<n;j++) {
     56:     for (k=n-1;k>=j;k--) {
     57:       d = sx[k]-sx[k-j];
     58:       if (PetscUnlikely(d == 0.0)) sf[k] = 0.0;  /* assume that the derivative is 0.0 and apply the Hermite interpolation */
     59:       else sf[k] = (sf[k]-sf[k-1]) / d;
     60:     }
     61:     sa[j] = sf[j];
     62:   }
     63:   return 0;
     64: }

     66: /*
     67:    FILTLAN function HermiteBaseFilterInChebyshevBasis

     69:    compute a base filter P(z) which is a continuous, piecewise polynomial P(z) expanded
     70:    in a basis of `translated' (i.e. scale-and-shift) Chebyshev polynomials in each interval

     72:    The base filter P(z) equals P_j(z) for z in the j-th interval [intv(j), intv(j+1)), where
     73:    P_j(z) a Hermite interpolating polynomial

     75:    input:
     76:    intv is a vector which defines the intervals; the j-th interval is [intv(j), intv(j+1))
     77:    HiLowFlags determines the shape of the base filter P(z)
     78:    Consider the j-th interval [intv(j), intv(j+1)]
     79:    HighLowFlag[j-1]==1,  P(z)==1 for z in [intv(j), intv(j+1)]
     80:                    ==0,  P(z)==0 for z in [intv(j), intv(j+1)]
     81:                    ==-1, [intv(j), intv(j+1)] is a transition interval;
     82:                          P(intv(j)) and P(intv(j+1)) are defined such that P(z) is continuous
     83:    baseDeg is the degree of smoothness of the Hermite (piecewise polynomial) interpolation
     84:    to be precise, the i-th derivative of P(z) is zero, i.e. d^{i}P(z)/dz^i==0, at all interval
     85:    end points z=intv(j) for i=1,...,baseDeg

     87:    output:
     88:    P(z) expanded in a basis of `translated' (scale-and-shift) Chebyshev polynomials
     89:    to be precise, for z in the j-th interval [intv(j),intv(j+1)), P(z) equals
     90:        P_j(z) = pp(1,j)*S_0(z) + pp(2,j)*S_1(z) + ... + pp(n,j)*S_{n-1}(z),
     91:    where S_i(z) is the `translated' Chebyshev polynomial in that interval,
     92:        S_i((z-c)/h) = T_i(z),  c = (intv(j)+intv(j+1))) / 2,  h = (intv(j+1)-intv(j)) / 2,
     93:    with T_i(z) the Chebyshev polynomial of the first kind,
     94:        T_0(z) = 1, T_1(z) = z, and T_i(z) = 2*z*T_{i-1}(z) - T_{i-2}(z) for i>=2
     95:    the return matrix is the matrix of Chebyshev coefficients pp just described

     97:    note that the degree of P(z) in each interval is (at most) 2*baseDeg+1, with 2*baseDeg+2 coefficients
     98:    let n be the length of intv; then there are n-1 intervals
     99:    therefore the return matrix pp is of size (2*baseDeg+2)-by-(n-1)
    100: */
    101: static PetscErrorCode FILTLAN_HermiteBaseFilterInChebyshevBasis(PetscReal *baseFilter,PetscReal *intv,PetscInt npoints,const PetscInt *HighLowFlags,PetscInt baseDeg)
    102: {
    103:   PetscInt       m,ii,jj;
    104:   PetscReal      flag,flag0,flag2,aa,bb,*px,*py,*sx,*sy,*pp,*qq,*sq,*sbf,*work,*currentPoint = intv;
    105:   const PetscInt *hilo = HighLowFlags;

    107:   m = 2*baseDeg+2;
    108:   jj = npoints-1;  /* jj is initialized as the number of intervals */
    109:   PetscMalloc5(m,&px,m,&py,m,&pp,m,&qq,m,&work);
    110:   sbf = baseFilter;

    112:   while (jj--) {  /* the main loop to compute the Chebyshev coefficients */

    114:     flag = (PetscReal)(*hilo++);  /* get the flag of the current interval */
    115:     if (flag == -1.0) {  /* flag == -1 means that the current interval is a transition polynomial */

    117:       flag2 = (PetscReal)(*hilo);  /* get flag2, the flag of the next interval */
    118:       flag0 = 1.0-flag2;       /* the flag of the previous interval is 1-flag2 */

    120:       /* two pointers for traversing x[] and y[] */
    121:       sx = px;
    122:       sy = py;

    124:       /* find the current interval [aa,bb] */
    125:       aa = *currentPoint++;
    126:       bb = *currentPoint;

    128:       /* now left-hand side */
    129:       ii = baseDeg+1;
    130:       while (ii--) {
    131:         *sy++ = flag0;
    132:         *sx++ = aa;
    133:       }

    135:       /* now right-hand side */
    136:       ii = baseDeg+1;
    137:       while (ii--) {
    138:         *sy++ = flag2;
    139:         *sx++ = bb;
    140:       }

    142:       /* build a Newton polynomial (indeed, the generalized Hermite interpolating polynomial) with x[] and y[] */
    143:       FILTLAN_NewtonPolynomial(m,px,py,pp,work);

    145:       /* pp contains coefficients of the Newton polynomial P(z) in the current interval [aa,bb], where
    146:          P(z) = pp(1) + pp(2)*(z-px(1)) + pp(3)*(z-px(1))*(z-px(2)) + ... + pp(n)*(z-px(1))*...*(z-px(n-1)) */

    148:       /* translate the Newton coefficients to the Chebyshev coefficients */
    149:       FILTLAN_ExpandNewtonPolynomialInChebyshevBasis(m,aa,bb,pp,px,qq,work);
    150:       /* qq contains coefficients of the polynomial in [aa,bb] in the `translated' Chebyshev basis */

    152:       /* copy the Chebyshev coefficients to baseFilter
    153:          OCTAVE/MATLAB: B(:,j) = qq, where j = (npoints-1)-jj and B is the return matrix */
    154:       sq = qq;
    155:       ii = 2*baseDeg+2;
    156:       while (ii--) *sbf++ = *sq++;

    158:     } else {

    160:       /* a constant polynomial P(z)=flag, where either flag==0 or flag==1
    161:        OCTAVE/MATLAB: B(1,j) = flag, where j = (npoints-1)-jj and B is the return matrix */
    162:       *sbf++ = flag;

    164:       /* the other coefficients are all zero, since P(z) is a constant
    165:        OCTAVE/MATLAB: B(1,j) = 0, where j = (npoints-1)-jj and B is the return matrix */
    166:       ii = 2*baseDeg+1;
    167:       while (ii--) *sbf++ = 0.0;

    169:       /* for the next point */
    170:       currentPoint++;
    171:     }
    172:   }
    173:   PetscFree5(px,py,pp,qq,work);
    174:   return 0;
    175: }

    177: /* ////////////////////////////////////////////////////////////////////////////
    178:    //    Base Filter
    179:    //////////////////////////////////////////////////////////////////////////// */

    181: /*
    182:    FILTLAN function GetIntervals

    184:    this routine determines the intervals (including the transition one(s)) by an iterative process

    186:    frame is a vector consisting of 4 ordered elements:
    187:        [frame(1),frame(4)] is the interval which (tightly) contains all eigenvalues, and
    188:        [frame(2),frame(3)] is the interval in which the eigenvalues are sought
    189:    baseDeg is the left-and-right degree of the base filter for each interval
    190:    polyDeg is the (maximum possible) degree of s(z), with z*s(z) the polynomial filter
    191:    intv is the output; the j-th interval is [intv(j),intv(j+1))
    192:    opts is a collection of interval options

    194:    the base filter P(z) is a piecewise polynomial from Hermite interpolation with degree baseDeg
    195:    at each end point of intervals

    197:    the polynomial filter Q(z) is in the form z*s(z), i.e. Q(0)==0, such that ||(1-P(z))-z*s(z)||_w is
    198:    minimized with s(z) a polynomial of degree up to polyDeg

    200:    the resulting polynomial filter Q(z) satisfies Q(x)>=Q(y) for x in [frame[1],frame[2]], and
    201:    y in [frame[0],frame[3]] but not in [frame[1],frame[2]]

    203:    the routine fills a PolynomialFilterInfo struct which gives some information of the polynomial filter
    204: */
    205: static PetscErrorCode FILTLAN_GetIntervals(PetscReal *intervals,PetscReal *frame,PetscInt polyDeg,PetscInt baseDeg,FILTLAN_IOP opts,FILTLAN_PFI filterInfo)
    206: {
    207:   PetscReal       intv[6],x,y,z1,z2,c,c1,c2,fc,fc2,halfPlateau,leftDelta,rightDelta,gridSize;
    208:   PetscReal       yLimit,ySummit,yLeftLimit,yRightLimit,bottom,qIndex,*baseFilter,*polyFilter;
    209:   PetscReal       yLimitGap=0.0,yLeftSummit=0.0,yLeftBottom=0.0,yRightSummit=0.0,yRightBottom=0.0;
    210:   PetscInt        i,ii,npoints,numIter,numLeftSteps=1,numRightSteps=1,numMoreLooked=0;
    211:   PetscBool       leftBoundaryMet=PETSC_FALSE,rightBoundaryMet=PETSC_FALSE,stepLeft,stepRight;
    212:   const PetscReal a=frame[0],a1=frame[1],b1=frame[2],b=frame[3];
    213:   const PetscInt  HighLowFlags[5] = { 1, -1, 0, -1, 1 };  /* if filterType is 1, only first 3 elements will be used */
    214:   const PetscInt  numLookMore = 2*(PetscInt)(0.5+(PetscLogReal(2.0)/PetscLogReal(opts->shiftStepExpanRate)));

    218:   filterInfo->filterType = 2;      /* mid-pass filter, for interior eigenvalues */
    219:   if (b == b1) {
    221:     filterInfo->filterType = 1;    /* high-pass filter, for largest eigenvalues */

    224:   /* the following recipe follows Yousef Saad (2005, 2006) with a few minor adaptations / enhancements */
    225:   halfPlateau = 0.5*(b1-a1)*opts->initialPlateau;    /* half length of the "plateau" of the (dual) base filter */
    226:   leftDelta = (b1-a1)*opts->initialShiftStep;        /* initial left shift */
    227:   rightDelta = leftDelta;                            /* initial right shift */
    228:   opts->numGridPoints = PetscMax(opts->numGridPoints,(PetscInt)(2.0*(b-a)/halfPlateau));
    229:   gridSize = (b-a) / (PetscReal)(opts->numGridPoints);

    231:   for (i=0;i<6;i++) intv[i] = 0.0;
    232:   if (filterInfo->filterType == 2) {  /* for interior eigenvalues */
    233:     npoints = 6;
    234:     intv[0] = a;
    235:     intv[5] = b;
    236:     /* intv[1], intv[2], intv[3], and intv[4] to be determined */
    237:   } else { /* filterType == 1 (or 3 with conversion), for extreme eigenvalues */
    238:     npoints = 4;
    239:     intv[0] = a;
    240:     intv[3] = b;
    241:     /* intv[1], and intv[2] to be determined */
    242:   }
    243:   z1 = a1 - leftDelta;
    244:   z2 = b1 + rightDelta;
    245:   filterInfo->filterOK = 0;  /* not yet found any OK filter */

    247:   /* allocate matrices */
    248:   PetscMalloc2((polyDeg+2)*(npoints-1),&polyFilter,(2*baseDeg+2)*(npoints-1),&baseFilter);

    250:   /* initialize the intervals, mainly for the case opts->maxOuterIter == 0 */
    251:   intervals[0] = intv[0];
    252:   intervals[3] = intv[3];
    253:   intervals[5] = intv[5];
    254:   intervals[1] = z1;
    255:   if (filterInfo->filterType == 2) {  /* a mid-pass filter for interior eigenvalues */
    256:     intervals[4] = z2;
    257:     c = (a1+b1) / 2.0;
    258:     intervals[2] = c - halfPlateau;
    259:     intervals[3] = c + halfPlateau;
    260:   } else {  /* filterType == 1 (or 3 with conversion) for extreme eigenvalues */
    261:     intervals[2] = z1 + (b1-z1)*opts->transIntervalRatio;
    262:   }

    264:   /* the main loop */
    265:   for (numIter=1;numIter<=opts->maxOuterIter;numIter++) {
    266:     if (z1 <= a) {  /* outer loop updates z1 and z2 */
    267:       z1 = a;
    268:       leftBoundaryMet = PETSC_TRUE;
    269:     }
    270:     if (filterInfo->filterType == 2) {  /* a <= z1 < (a1) */
    271:       if (z2 >= b) {  /* a mid-pass filter for interior eigenvalues */
    272:         z2 = b;
    273:         rightBoundaryMet = PETSC_TRUE;
    274:       }
    275:       /* a <= z1 < c-h < c+h < z2 <= b, where h is halfPlateau */
    276:       /* [z1, c-h] and [c+h, z2] are transition interval */
    277:       intv[1] = z1;
    278:       intv[4] = z2;
    279:       c1 = z1 + halfPlateau;
    280:       intv[2] = z1;           /* i.e. c1 - halfPlateau */
    281:       intv[3] = c1 + halfPlateau;
    282:       FILTLAN_HermiteBaseFilterInChebyshevBasis(baseFilter,intv,6,HighLowFlags,baseDeg);
    283:       FILTLAN_FilteredConjugateResidualPolynomial(polyFilter,baseFilter,2*baseDeg+2,intv,6,opts->intervalWeights,polyDeg);
    284:       /* fc1 = FILTLAN_PiecewisePolynomialEvaluationInChebyshevBasis(polyFilter,polyDeg+2,intv,npoints,b1) - FILTLAN_PiecewisePolynomialEvaluationInChebyshevBasis(polyFilter,polyDeg+2,intv,npoints,a1); */
    285:       c2 = z2 - halfPlateau;
    286:       intv[2] = c2 - halfPlateau;
    287:       intv[3] = z2;           /* i.e. c2 + halfPlateau */
    288:       FILTLAN_HermiteBaseFilterInChebyshevBasis(baseFilter,intv,6,HighLowFlags,baseDeg);
    289:       FILTLAN_FilteredConjugateResidualPolynomial(polyFilter,baseFilter,2*baseDeg+2,intv,6,opts->intervalWeights,polyDeg);
    290:       fc2 = FILTLAN_PiecewisePolynomialEvaluationInChebyshevBasis(polyFilter,polyDeg+2,intv,npoints,b1) - FILTLAN_PiecewisePolynomialEvaluationInChebyshevBasis(polyFilter,polyDeg+2,intv,npoints,a1);
    291:       yLimitGap = PETSC_MAX_REAL;
    292:       ii = opts->maxInnerIter;
    293:       while (ii-- && !(yLimitGap <= opts->yLimitTol)) {
    294:         /* recursive bisection to get c such that p(a1) are p(b1) approximately the same */
    295:         c = (c1+c2) / 2.0;
    296:         intv[2] = c - halfPlateau;
    297:         intv[3] = c + halfPlateau;
    298:         FILTLAN_HermiteBaseFilterInChebyshevBasis(baseFilter,intv,6,HighLowFlags,baseDeg);
    299:         FILTLAN_FilteredConjugateResidualPolynomial(polyFilter,baseFilter,2*baseDeg+2,intv,6,opts->intervalWeights,polyDeg);
    300:         fc = FILTLAN_PiecewisePolynomialEvaluationInChebyshevBasis(polyFilter,polyDeg+2,intv,npoints,b1) - FILTLAN_PiecewisePolynomialEvaluationInChebyshevBasis(polyFilter,polyDeg+2,intv,npoints,a1);
    301:         if (fc*fc2 < 0.0) {
    302:           c1 = c;
    303:           /* fc1 = fc; */
    304:         } else {
    305:           c2 = c;
    306:           fc2 = fc;
    307:         }
    308:         yLimitGap = PetscAbsReal(fc);
    309:       }
    310:     } else {  /* filterType == 1 (or 3 with conversion) for extreme eigenvalues */
    311:       intv[1] = z1;
    312:       intv[2] = z1 + (b1-z1)*opts->transIntervalRatio;
    313:       FILTLAN_HermiteBaseFilterInChebyshevBasis(baseFilter,intv,4,HighLowFlags,baseDeg);
    314:       FILTLAN_FilteredConjugateResidualPolynomial(polyFilter,baseFilter,2*baseDeg+2,intv,4,opts->intervalWeights,polyDeg);
    315:     }
    316:     /* polyFilter contains the coefficients of the polynomial filter which approximates phi(x)
    317:        expanded in the `translated' Chebyshev basis */
    318:     /* psi(x) = 1.0 - phi(x) is the dual base filter approximated by a polynomial in the form x*p(x) */
    319:     yLeftLimit  = 1.0 - FILTLAN_PiecewisePolynomialEvaluationInChebyshevBasis(polyFilter,polyDeg+2,intv,npoints,a1);
    320:     yRightLimit = 1.0 - FILTLAN_PiecewisePolynomialEvaluationInChebyshevBasis(polyFilter,polyDeg+2,intv,npoints,b1);
    321:     yLimit  = (yLeftLimit < yRightLimit) ? yLeftLimit : yRightLimit;
    322:     ySummit = (yLeftLimit > yRightLimit) ? yLeftLimit : yRightLimit;
    323:     x = a1;
    324:     while ((x+=gridSize) < b1) {
    325:       y = 1.0 - FILTLAN_PiecewisePolynomialEvaluationInChebyshevBasis(polyFilter,polyDeg+2,intv,npoints,x);
    326:       if (y < yLimit)  yLimit  = y;
    327:       if (y > ySummit) ySummit = y;
    328:     }
    329:     /* now yLimit is the minimum of x*p(x) for x in [a1, b1] */
    330:     stepLeft  = PETSC_FALSE;
    331:     stepRight = PETSC_FALSE;
    332:     if ((yLimit < yLeftLimit && yLimit < yRightLimit) || yLimit < opts->yBottomLine) {
    333:       /* very bad, step to see what will happen */
    334:       stepLeft = PETSC_TRUE;
    335:       if (filterInfo->filterType == 2) stepRight = PETSC_TRUE;
    336:     } else if (filterInfo->filterType == 2) {
    337:       if (yLeftLimit < yRightLimit) {
    338:         if (yRightLimit-yLeftLimit > opts->yLimitTol) stepLeft = PETSC_TRUE;
    339:       } else if (yLeftLimit-yRightLimit > opts->yLimitTol) stepRight = PETSC_TRUE;
    340:     }
    341:     if (!stepLeft) {
    342:       yLeftBottom = yLeftLimit;
    343:       x = a1;
    344:       while ((x-=gridSize) >= a) {
    345:         y = 1.0 - FILTLAN_PiecewisePolynomialEvaluationInChebyshevBasis(polyFilter,polyDeg+2,intv,npoints,x);
    346:         if (y < yLeftBottom) yLeftBottom = y;
    347:         else if (y > yLeftBottom) break;
    348:       }
    349:       yLeftSummit = yLeftBottom;
    350:       while ((x-=gridSize) >= a) {
    351:         y = 1.0 - FILTLAN_PiecewisePolynomialEvaluationInChebyshevBasis(polyFilter,polyDeg+2,intv,npoints,x);
    352:         if (y > yLeftSummit) {
    353:           yLeftSummit = y;
    354:           if (yLeftSummit > yLimit*opts->yRippleLimit) {
    355:             stepLeft = PETSC_TRUE;
    356:             break;
    357:           }
    358:         }
    359:         if (y < yLeftBottom) yLeftBottom = y;
    360:       }
    361:     }
    362:     if (filterInfo->filterType == 2 && !stepRight) {
    363:       yRightBottom = yRightLimit;
    364:       x = b1;
    365:       while ((x+=gridSize) <= b) {
    366:         y = 1.0 - FILTLAN_PiecewisePolynomialEvaluationInChebyshevBasis(polyFilter,polyDeg+2,intv,npoints,x);
    367:         if (y < yRightBottom) yRightBottom = y;
    368:         else if (y > yRightBottom) break;
    369:       }
    370:       yRightSummit = yRightBottom;
    371:       while ((x+=gridSize) <= b) {
    372:         y = 1.0 - FILTLAN_PiecewisePolynomialEvaluationInChebyshevBasis(polyFilter,polyDeg+2,intv,npoints,x);
    373:         if (y > yRightSummit) {
    374:           yRightSummit = y;
    375:           if (yRightSummit > yLimit*opts->yRippleLimit) {
    376:             stepRight = PETSC_TRUE;
    377:             break;
    378:           }
    379:         }
    380:         if (y < yRightBottom) yRightBottom = y;
    381:       }
    382:     }
    383:     if (!stepLeft && !stepRight) {
    384:       if (filterInfo->filterType == 2) bottom = PetscMin(yLeftBottom,yRightBottom);
    385:       else bottom = yLeftBottom;
    386:       qIndex = 1.0 - (yLimit-bottom) / (ySummit-bottom);
    387:       if (filterInfo->filterOK == 0 || filterInfo->filterQualityIndex < qIndex) {
    388:         /* found the first OK filter or a better filter */
    389:         for (i=0;i<6;i++) intervals[i] = intv[i];
    390:         filterInfo->filterOK           = 1;
    391:         filterInfo->filterQualityIndex = qIndex;
    392:         filterInfo->numIter            = numIter;
    393:         filterInfo->yLimit             = yLimit;
    394:         filterInfo->ySummit            = ySummit;
    395:         filterInfo->numLeftSteps       = numLeftSteps;
    396:         filterInfo->yLeftSummit        = yLeftSummit;
    397:         filterInfo->yLeftBottom        = yLeftBottom;
    398:         if (filterInfo->filterType == 2) {
    399:           filterInfo->yLimitGap        = yLimitGap;
    400:           filterInfo->numRightSteps    = numRightSteps;
    401:           filterInfo->yRightSummit     = yRightSummit;
    402:           filterInfo->yRightBottom     = yRightBottom;
    403:         }
    404:         numMoreLooked = 0;
    405:       } else if (++numMoreLooked == numLookMore) {
    406:         /* filter has been optimal */
    407:         filterInfo->filterOK = 2;
    408:         break;
    409:       }
    410:       /* try stepping further to see whether it can improve */
    411:       stepLeft = PETSC_TRUE;
    412:       if (filterInfo->filterType == 2) stepRight = PETSC_TRUE;
    413:     }
    414:     /* check whether we can really "step" */
    415:     if (leftBoundaryMet) {
    416:       if (filterInfo->filterType == 1 || rightBoundaryMet) break;  /* cannot step further, so break the loop */
    417:       if (stepLeft) {
    418:         /* cannot step left, so try stepping right */
    419:         stepLeft  = PETSC_FALSE;
    420:         stepRight = PETSC_TRUE;
    421:       }
    422:     }
    423:     if (rightBoundaryMet && stepRight) {
    424:       /* cannot step right, so try stepping left */
    425:       stepRight = PETSC_FALSE;
    426:       stepLeft  = PETSC_TRUE;
    427:     }
    428:     /* now "step" */
    429:     if (stepLeft) {
    430:       numLeftSteps++;
    431:       if (filterInfo->filterType == 2) leftDelta *= opts->shiftStepExpanRate; /* expand the step for faster convergence */
    432:       z1 -= leftDelta;
    433:     }
    434:     if (stepRight) {
    435:       numRightSteps++;
    436:       rightDelta *= opts->shiftStepExpanRate;  /* expand the step for faster convergence */
    437:       z2 += rightDelta;
    438:     }
    439:     if (filterInfo->filterType == 2) {
    440:       /* shrink the "plateau" of the (dual) base filter */
    441:       if (stepLeft && stepRight) halfPlateau /= opts->plateauShrinkRate;
    442:       else halfPlateau /= PetscSqrtReal(opts->plateauShrinkRate);
    443:     }
    444:   }

    447:   filterInfo->totalNumIter = numIter;
    448:   PetscFree2(polyFilter,baseFilter);
    449:   return 0;
    450: }

    452: /* ////////////////////////////////////////////////////////////////////////////
    453:    //    Chebyshev Polynomials
    454:    //////////////////////////////////////////////////////////////////////////// */

    456: /*
    457:    FILTLAN function ExpandNewtonPolynomialInChebyshevBasis

    459:    translate the coefficients of a Newton polynomial to the coefficients
    460:    in a basis of the `translated' (scale-and-shift) Chebyshev polynomials

    462:    input:
    463:    a Newton polynomial defined by vectors a and x:
    464:        P(z) = a(1) + a(2)*(z-x(1)) + a(3)*(z-x(1))*(z-x(2)) + ... + a(n)*(z-x(1))*...*(z-x(n-1))
    465:    the interval [aa,bb] defines the `translated' Chebyshev polynomials S_i(z) = T_i((z-c)/h),
    466:        where c=(aa+bb)/2 and h=(bb-aa)/2, and T_i is the Chebyshev polynomial of the first kind
    467:    note that T_i is defined by T_0(z)=1, T_1(z)=z, and T_i(z)=2*z*T_{i-1}(z)+T_{i-2}(z) for i>=2

    469:    output:
    470:    a vector q containing the Chebyshev coefficients:
    471:        P(z) = q(1)*S_0(z) + q(2)*S_1(z) + ... + q(n)*S_{n-1}(z)
    472: */
    473: static inline PetscErrorCode FILTLAN_ExpandNewtonPolynomialInChebyshevBasis(PetscInt n,PetscReal aa,PetscReal bb,PetscReal *a,PetscReal *x,PetscReal *q,PetscReal *q2)
    474: {
    475:   PetscInt  m,mm;
    476:   PetscReal *sa,*sx,*sq,*sq2,c,c2,h,h2;

    478:   sa = a+n;    /* pointers for traversing a and x */
    479:   sx = x+n-1;
    480:   *q = *--sa;  /* set q[0] = a(n) */

    482:   c = (aa+bb)/2.0;
    483:   h = (bb-aa)/2.0;
    484:   h2 = h/2.0;

    486:   for (m=1;m<=n-1;m++) {  /* the main loop for translation */

    488:     /* compute q2[0:m-1] = (c-x[n-m-1])*q[0:m-1] */
    489:     mm = m;
    490:     sq = q;
    491:     sq2 = q2;
    492:     c2 = c-(*--sx);
    493:     while (mm--) *(sq2++) = c2*(*sq++);
    494:     *sq2 = 0.0;         /* q2[m] = 0.0 */
    495:     *(q2+1) += h*(*q);  /* q2[1] = q2[1] + h*q[0] */

    497:     /* compute q2[0:m-2] = q2[0:m-2] + h2*q[1:m-1] */
    498:     mm = m-1;
    499:     sq2 = q2;
    500:     sq = q+1;
    501:     while (mm--) *(sq2++) += h2*(*sq++);

    503:     /* compute q2[2:m] = q2[2:m] + h2*q[1:m-1] */
    504:     mm = m-1;
    505:     sq2 = q2+2;
    506:     sq = q+1;
    507:     while (mm--) *(sq2++) += h2*(*sq++);

    509:     /* compute q[0:m] = q2[0:m] */
    510:     mm = m+1;
    511:     sq2 = q2;
    512:     sq = q;
    513:     while (mm--) *sq++ = *sq2++;
    514:     *q += (*--sa);      /* q[0] = q[0] + p[n-m-1] */
    515:   }
    516:   return 0;
    517: }

    519: /*
    520:    FILTLAN function PolynomialEvaluationInChebyshevBasis

    522:    evaluate P(z) at z=z0, where P(z) is a polynomial expanded in a basis of
    523:    the `translated' (i.e. scale-and-shift) Chebyshev polynomials

    525:    input:
    526:    c is a vector of Chebyshev coefficients which defines the polynomial
    527:        P(z) = c(1)*S_0(z) + c(2)*S_1(z) + ... + c(n)*S_{n-1}(z),
    528:    where S_i is the `translated' Chebyshev polynomial S_i((z-c)/h) = T_i(z), with
    529:        c = (intv(j)+intv(j+1)) / 2,  h = (intv(j+1)-intv(j)) / 2
    530:    note that T_i(z) is the Chebyshev polynomial of the first kind,
    531:        T_0(z) = 1, T_1(z) = z, and T_i(z) = 2*z*T_{i-1}(z) - T_{i-2}(z) for i>=2

    533:    output:
    534:    the evaluated value of P(z) at z=z0
    535: */
    536: static inline PetscReal FILTLAN_PolynomialEvaluationInChebyshevBasis(PetscReal *pp,PetscInt m,PetscInt idx,PetscReal z0,PetscReal aa,PetscReal bb)
    537: {
    538:   PetscInt  ii,deg1;
    539:   PetscReal y,zz,t0,t1,t2,*sc;

    541:   deg1 = m;
    542:   if (aa==-1.0 && bb==1.0) zz = z0;  /* treat it as a special case to reduce rounding errors */
    543:   else zz = (aa==bb)? 0.0 : -1.0+2.0*(z0-aa)/(bb-aa);

    545:   /* compute y = P(z0), where we utilize the Chebyshev recursion */
    546:   sc = pp+(idx-1)*m;   /* sc points to column idx of pp */
    547:   y = *sc++;
    548:   t1 = 1.0; t2 = zz;
    549:   ii = deg1-1;
    550:   while (ii--) {
    551:     /* Chebyshev recursion: T_0(zz)=1, T_1(zz)=zz, and T_{i+1}(zz) = 2*zz*T_i(zz) + T_{i-1}(zz) for i>=2
    552:        the values of T_{i+1}(zz), T_i(zz), T_{i-1}(zz) are stored in t0, t1, t2, respectively */
    553:     t0 = 2*zz*t1 - t2;
    554:     /* it also works for the base case / the first iteration, where t0 equals 2*zz*1-zz == zz which is T_1(zz) */
    555:     t2 = t1;
    556:     t1 = t0;
    557:     y += (*sc++)*t0;
    558:   }
    559:   return y;
    560: }

    562: #define basisTranslated PETSC_TRUE
    563: /*
    564:    FILTLAN function PiecewisePolynomialEvaluationInChebyshevBasis

    566:    evaluate P(z) at z=z0, where P(z) is a piecewise polynomial expanded
    567:    in a basis of the (optionally translated, i.e. scale-and-shift) Chebyshev polynomials for each interval

    569:    input:
    570:    intv is a vector which defines the intervals; the j-th interval is [intv(j), intv(j+1))
    571:    pp is a matrix of Chebyshev coefficients which defines a piecewise polynomial P(z)
    572:    in a basis of the `translated' Chebyshev polynomials in each interval
    573:    the polynomial P_j(z) in the j-th interval, i.e. when z is in [intv(j), intv(j+1)), is defined by the j-th column of pp:
    574:        if basisTranslated == false, then
    575:            P_j(z) = pp(1,j)*T_0(z) + pp(2,j)*T_1(z) + ... + pp(n,j)*T_{n-1}(z),
    576:        where T_i(z) is the Chebyshev polynomial of the first kind,
    577:            T_0(z) = 1, T_1(z) = z, and T_i(z) = 2*z*T_{i-1}(z) - T_{i-2}(z) for i>=2
    578:        if basisTranslated == true, then
    579:            P_j(z) = pp(1,j)*S_0(z) + pp(2,j)*S_1(z) + ... + pp(n,j)*S_{n-1}(z),
    580:        where S_i is the `translated' Chebyshev polynomial S_i((z-c)/h) = T_i(z), with
    581:            c = (intv(j)+intv(j+1)) / 2,  h = (intv(j+1)-intv(j)) / 2

    583:    output:
    584:    the evaluated value of P(z) at z=z0

    586:    note that if z0 falls below the first interval, then the polynomial in the first interval will be used to evaluate P(z0)
    587:              if z0 flies over  the last  interval, then the polynomial in the last  interval will be used to evaluate P(z0)
    588: */
    589: static inline PetscReal FILTLAN_PiecewisePolynomialEvaluationInChebyshevBasis(PetscReal *pp,PetscInt m,PetscReal *intv,PetscInt npoints,PetscReal z0)
    590: {
    591:   PetscReal *sintv,aa,bb,resul;
    592:   PetscInt  idx;

    594:   /* determine the interval which contains z0 */
    595:   sintv = &intv[1];
    596:   idx = 1;
    597:   if (npoints>2 && z0 >= *sintv) {
    598:     sintv++;
    599:     while (++idx < npoints-1) {
    600:       if (z0 < *sintv) break;
    601:       sintv++;
    602:     }
    603:   }
    604:   /* idx==1 if npoints<=2; otherwise idx satisfies:
    605:          intv(idx) <= z0 < intv(idx+1),  if 2 <= idx <= npoints-2
    606:          z0 < intv(idx+1),               if idx == 1
    607:          intv(idx) <= z0,                if idx == npoints-1
    608:      in addition, sintv points to &intv(idx+1) */
    609:   if (basisTranslated) {
    610:     /* the basis consists of `translated' Chebyshev polynomials */
    611:     /* find the interval of concern, [aa,bb] */
    612:     aa = *(sintv-1);
    613:     bb = *sintv;
    614:     resul = FILTLAN_PolynomialEvaluationInChebyshevBasis(pp,m,idx,z0,aa,bb);
    615:   } else {
    616:     /* the basis consists of standard Chebyshev polynomials, with interval [-1.0,1.0] for integration */
    617:     resul = FILTLAN_PolynomialEvaluationInChebyshevBasis(pp,m,idx,z0,-1.0,1.0);
    618:   }
    619:   return resul;
    620: }

    622: /*
    623:    FILTLAN function PiecewisePolynomialInnerProductInChebyshevBasis

    625:    compute the weighted inner product of two piecewise polynomials expanded
    626:    in a basis of `translated' (i.e. scale-and-shift) Chebyshev polynomials for each interval

    628:    pp and qq are two matrices of Chebyshev coefficients which define the piecewise polynomials P(z) and Q(z), respectively
    629:    for z in the j-th interval, P(z) equals
    630:        P_j(z) = pp(1,j)*S_0(z) + pp(2,j)*S_1(z) + ... + pp(n,j)*S_{n-1}(z),
    631:    and Q(z) equals
    632:        Q_j(z) = qq(1,j)*S_0(z) + qq(2,j)*S_1(z) + ... + qq(n,j)*S_{n-1}(z),
    633:    where S_i(z) is the `translated' Chebyshev polynomial in that interval,
    634:        S_i((z-c)/h) = T_i(z),  c = (aa+bb)) / 2,  h = (bb-aa) / 2,
    635:    with T_i(z) the Chebyshev polynomial of the first kind
    636:        T_0(z) = 1, T_1(z) = z, and T_i(z) = 2*z*T_{i-1}(z) - T_{i-2}(z) for i>=2

    638:    the (scaled) j-th interval inner product is defined by
    639:        <P_j,Q_j> = (Pi/2)*(pp(1,j)*qq(1,j) + sum_{k} pp(k,j)*qq(k,j)),
    640:    which comes from the property
    641:        <T_0,T_0>=pi, <T_i,T_i>=pi/2 for i>=1, and <T_i,T_j>=0 for i!=j

    643:    the weighted inner product is <P,Q> = sum_{j} intervalWeights(j)*<P_j,Q_j>,
    644:    which is the return value

    646:    note that for unit weights, pass an empty vector of intervalWeights (i.e. of length 0)
    647: */
    648: static inline PetscReal FILTLAN_PiecewisePolynomialInnerProductInChebyshevBasis(PetscReal *pp,PetscInt prows,PetscInt pcols,PetscInt ldp,PetscReal *qq,PetscInt qrows,PetscInt qcols,PetscInt ldq,const PetscReal *intervalWeights)
    649: {
    650:   PetscInt  nintv,deg1,i,k;
    651:   PetscReal *sp,*sq,ans=0.0,ans2;

    653:   deg1 = PetscMin(prows,qrows);  /* number of effective coefficients, one more than the effective polynomial degree */
    654:   if (PetscUnlikely(!deg1)) PetscFunctionReturn(0.0);
    655:   nintv = PetscMin(pcols,qcols);  /* number of intervals */

    657:   /* scaled by intervalWeights(i) in the i-th interval (we assume intervalWeights[] are always provided).
    658:      compute ans = sum_{i=1,...,nintv} intervalWeights(i)*[ pp(1,i)*qq(1,i) + sum_{k=1,...,deg} pp(k,i)*qq(k,i) ] */
    659:   for (i=0;i<nintv;i++) {   /* compute ans2 = pp(1,i)*qq(1,i) + sum_{k=1,...,deg} pp(k,i)*qq(k,i) */
    660:     sp = pp+i*ldp;
    661:     sq = qq+i*ldq;
    662:     ans2 = (*sp) * (*sq);  /* the first term pp(1,i)*qq(1,i) is being added twice */
    663:     for (k=0;k<deg1;k++) ans2 += (*sp++)*(*sq++);  /* add pp(k,i)*qq(k,i) */
    664:     ans += ans2*intervalWeights[i];  /* compute ans += ans2*intervalWeights(i) */
    665:   }
    666:   PetscFunctionReturn(ans*PETSC_PI/2.0);
    667: }

    669: /*
    670:    FILTLAN function PiecewisePolynomialInChebyshevBasisMultiplyX

    672:    compute Q(z) = z*P(z), where P(z) and Q(z) are piecewise polynomials expanded
    673:    in a basis of `translated' (i.e. scale-and-shift) Chebyshev polynomials for each interval

    675:    P(z) and Q(z) are stored as matrices of Chebyshev coefficients pp and qq, respectively

    677:    For z in the j-th interval, P(z) equals
    678:        P_j(z) = pp(1,j)*S_0(z) + pp(2,j)*S_1(z) + ... + pp(n,j)*S_{n-1}(z),
    679:    and Q(z) equals
    680:        Q_j(z) = qq(1,j)*S_0(z) + qq(2,j)*S_1(z) + ... + qq(n,j)*S_{n-1}(z),
    681:    where S_i(z) is the `translated' Chebyshev polynomial in that interval,
    682:        S_i((z-c)/h) = T_i(z),  c = (intv(j)+intv(j+1))) / 2,  h = (intv(j+1)-intv(j)) / 2,
    683:    with T_i(z) the Chebyshev polynomial of the first kind
    684:        T_0(z) = 1, T_1(z) = z, and T_i(z) = 2*z*T_{i-1}(z) - T_{i-2}(z) for i>=2

    686:    the returned matrix is qq which represents Q(z) = z*P(z)
    687: */
    688: static inline PetscErrorCode FILTLAN_PiecewisePolynomialInChebyshevBasisMultiplyX(PetscReal *pp,PetscInt deg1,PetscInt ldp,PetscReal *intv,PetscInt nintv,PetscReal *qq,PetscInt ldq)
    689: {
    690:   PetscInt  i,j;
    691:   PetscReal c,h,h2,tmp,*sp,*sq,*sp2,*sq2;

    693:   for (j=0;j<nintv;j++) {    /* consider interval between intv(j) and intv(j+1) */
    694:     sp = pp+j*ldp;
    695:     sq = qq+j*ldq;
    696:     sp2 = sp;
    697:     sq2 = sq+1;
    698:     c = 0.5*(intv[j] + intv[j+1]);   /* compute c = (intv(j) + intv(j+1))/2 */
    699:     h = 0.5*(intv[j+1] - intv[j]);   /* compute h = (intv(j+1) - intv(j))/2  and  h2 = h/2 */
    700:     h2 = 0.5*h;
    701:     i = deg1;
    702:     while (i--) *sq++ = c*(*sp++);    /* compute q(1:deg1,j) = c*p(1:deg1,j) */
    703:     *sq++ = 0.0;                      /* set q(deg1+1,j) = 0.0 */
    704:     *(sq2++) += h*(*sp2++);           /* compute q(2,j) = q(2,j) + h*p(1,j) */
    705:     i = deg1-1;
    706:     while (i--) {       /* compute q(3:deg1+1,j) += h2*p(2:deg1,j) and then q(1:deg1-1,j) += h2*p(2:deg1,j) */
    707:       tmp = h2*(*sp2++);
    708:       *(sq2-2) += tmp;
    709:       *(sq2++) += tmp;
    710:     }
    711:   }
    712:   return 0;
    713: }

    715: /* ////////////////////////////////////////////////////////////////////////////
    716:    //    Conjugate Residual Method in the Polynomial Space
    717:    //////////////////////////////////////////////////////////////////////////// */

    719: /*
    720:     B := alpha*A + beta*B

    722:     A,B are nxk
    723: */
    724: static inline PetscErrorCode Mat_AXPY_BLAS(PetscInt n,PetscInt k,PetscReal alpha,const PetscReal *A,PetscInt lda,PetscReal beta,PetscReal *B,PetscInt ldb)
    725: {
    726:   PetscInt       i,j;

    728:   if (beta==(PetscReal)1.0) {
    729:     for (j=0;j<k;j++) for (i=0;i<n;i++) B[i+j*ldb] += alpha*A[i+j*lda];
    730:     PetscLogFlops(2.0*n*k);
    731:   } else {
    732:     for (j=0;j<k;j++) for (i=0;i<n;i++) B[i+j*ldb] = alpha*A[i+j*lda] + beta*B[i+j*ldb];
    733:     PetscLogFlops(3.0*n*k);
    734:   }
    735:   return 0;
    736: }

    738: /*
    739:    FILTLAN function FilteredConjugateResidualPolynomial

    741:    ** Conjugate Residual Method in the Polynomial Space

    743:    this routine employs a conjugate-residual-type algorithm in polynomial space to minimize ||P(z)-Q(z)||_w,
    744:    where P(z), the base filter, is the input piecewise polynomial, and
    745:          Q(z) is the output polynomial satisfying Q(0)==1, i.e. the constant term of Q(z) is 1
    746:    niter is the number of conjugate-residual iterations; therefore, the degree of Q(z) is up to niter+1
    747:    both P(z) and Q(z) are expanded in the `translated' (scale-and-shift) Chebyshev basis for each interval,
    748:    and presented as matrices of Chebyshev coefficients, denoted by pp and qq, respectively

    750:    input:
    751:    intv is a vector which defines the intervals; the j-th interval is [intv(j),intv(j+1))
    752:    w is a vector of Chebyshev weights; the weight of j-th interval is w(j)
    753:        the interval weights define the inner product of two continuous functions and then
    754:        the derived w-norm ||P(z)-Q(z)||_w
    755:    pp is a matrix of Chebyshev coefficients which defines the piecewise polynomial P(z)
    756:    to be specific, for z in [intv(j), intv(j+1)), P(z) equals
    757:        P_j(z) = pp(1,j)*S_0(z) + pp(2,j)*S_1(z) + ... + pp(niter+2,j)*S_{niter+1}(z),
    758:    where S_i(z) is the `translated' Chebyshev polynomial in that interval,
    759:        S_i((z-c)/h) = T_i(z),  c = (intv(j)+intv(j+1))) / 2,  h = (intv(j+1)-intv(j)) / 2,
    760:    with T_i(z) the Chebyshev polynomial of the first kind,
    761:        T_0(z) = 1, T_1(z) = z, and T_i(z) = 2*z*T_{i-1}(z) - T_{i-2}(z) for i>=2

    763:    output:
    764:    the return matrix, denoted by qq, represents a polynomial Q(z) with degree up to 1+niter
    765:    and satisfying Q(0)==1, such that ||P(z))-Q(z)||_w is minimized
    766:    this polynomial Q(z) is expanded in the `translated' Chebyshev basis for each interval
    767:    to be precise, considering z in [intv(j), intv(j+1)), Q(z) equals
    768:        Q_j(z) = qq(1,j)*S_0(z) + qq(2,j)*S_1(z) + ... + qq(niter+2,j)*S_{niter+1}(z)

    770:    note:
    771:    1. since Q(0)==1, P(0)==1 is expected; if P(0)!=1, one can translate P(z)
    772:       for example, if P(0)==0, one can use 1-P(z) as input instead of P(z)
    773:    2. typically, the base filter, defined by pp and intv, is from Hermite interpolation
    774:       in intervals [intv(j),intv(j+1)) for j=1,...,nintv, with nintv the number of intervals
    775: */
    776: static PetscErrorCode FILTLAN_FilteredConjugateResidualPolynomial(PetscReal *cpol,PetscReal *baseFilter,PetscInt nbase,PetscReal *intv,PetscInt m,PetscReal *intervalWeights,PetscInt niter)
    777: {
    778:   PetscInt       i,j,srpol,scpol,sarpol,sppol,sappol,ld,nintv;
    779:   PetscReal      rho,rho0,rho1,den,bet,alp,alp0,*ppol,*rpol,*appol,*arpol;

    781:   nintv = m-1;
    782:   ld = niter+2;  /* leading dimension */
    783:   PetscCalloc4(ld*nintv,&ppol,ld*nintv,&rpol,ld*nintv,&appol,ld*nintv,&arpol);
    784:   PetscArrayzero(cpol,ld*nintv);
    785:   /* initialize polynomial ppol to be 1 (i.e. multiplicative identity) in all intervals */
    786:   sppol = 2;
    787:   srpol = 2;
    788:   scpol = 2;
    789:   for (j=0;j<nintv;j++) {
    790:     ppol[j*ld] = 1.0;
    791:     rpol[j*ld] = 1.0;
    792:     cpol[j*ld] = 1.0;
    793:   }
    794:   /* ppol is the initial p-polynomial (corresponding to the A-conjugate vector p in CG)
    795:      rpol is the r-polynomial (corresponding to the residual vector r in CG)
    796:      cpol is the "corrected" residual polynomial (result of this function) */
    797:   sappol = 3;
    798:   sarpol = 3;
    799:   FILTLAN_PiecewisePolynomialInChebyshevBasisMultiplyX(ppol,sppol,ld,intv,nintv,appol,ld);
    800:   for (i=0;i<3;i++) for (j=0;j<nintv;j++) arpol[i+j*ld] = appol[i+j*ld];
    801:   rho = FILTLAN_PiecewisePolynomialInnerProductInChebyshevBasis(rpol,srpol,nintv,ld,arpol,sarpol,nintv,ld,intervalWeights);
    802:   for (i=0;i<niter;i++) {
    803:     den = FILTLAN_PiecewisePolynomialInnerProductInChebyshevBasis(appol,sappol,nintv,ld,appol,sappol,nintv,ld,intervalWeights);
    804:     alp0 = rho/den;
    805:     rho1 = FILTLAN_PiecewisePolynomialInnerProductInChebyshevBasis(baseFilter,nbase,nintv,nbase,appol,sappol,nintv,ld,intervalWeights);
    806:     alp  = (rho-rho1)/den;
    807:     srpol++;
    808:     scpol++;
    809:     Mat_AXPY_BLAS(srpol,nintv,-alp0,appol,ld,1.0,rpol,ld);
    810:     Mat_AXPY_BLAS(scpol,nintv,-alp,appol,ld,1.0,cpol,ld);
    811:     if (i+1 == niter) break;
    812:     sarpol++;
    813:     FILTLAN_PiecewisePolynomialInChebyshevBasisMultiplyX(rpol,srpol,ld,intv,nintv,arpol,ld);
    814:     rho0 = rho;
    815:     rho = FILTLAN_PiecewisePolynomialInnerProductInChebyshevBasis(rpol,srpol,nintv,ld,arpol,sarpol,nintv,ld,intervalWeights);
    816:     bet  = rho / rho0;
    817:     sppol++;
    818:     sappol++;
    819:     Mat_AXPY_BLAS(sppol,nintv,1.0,rpol,ld,bet,ppol,ld);
    820:     Mat_AXPY_BLAS(sappol,nintv,1.0,arpol,ld,bet,appol,ld);
    821:   }
    822:   PetscFree4(ppol,rpol,appol,arpol);
    823:   return 0;
    824: }

    826: /*
    827:    FILTLAN function FilteredConjugateResidualMatrixPolynomialVectorProduct

    829:    this routine employs a conjugate-residual-type algorithm in polynomial space to compute
    830:    x = x0 + s(A)*r0 with r0 = b - A*x0, such that ||(1-P(z))-z*s(z)||_w is minimized, where
    831:    P(z) is a given piecewise polynomial, called the base filter,
    832:    s(z) is a polynomial of degree up to niter, the number of conjugate-residual iterations,
    833:    and b and x0 are given vectors

    835:    note that P(z) is expanded in the `translated' (scale-and-shift) Chebyshev basis for each interval,
    836:    and presented as a matrix of Chebyshev coefficients pp

    838:    input:
    839:    A is a sparse matrix
    840:    x0, b are vectors
    841:    niter is the number of conjugate-residual iterations
    842:    intv is a vector which defines the intervals; the j-th interval is [intv(j),intv(j+1))
    843:    w is a vector of Chebyshev weights; the weight of j-th interval is w(j)
    844:        the interval weights define the inner product of two continuous functions and then
    845:        the derived w-norm ||P(z)-Q(z)||_w
    846:    pp is a matrix of Chebyshev coefficients which defines the piecewise polynomial P(z)
    847:    to be specific, for z in [intv(j), intv(j+1)), P(z) equals
    848:        P_j(z) = pp(1,j)*S_0(z) + pp(2,j)*S_1(z) + ... + pp(niter+2,j)*S_{niter+1}(z),
    849:    where S_i(z) is the `translated' Chebyshev polynomial in that interval,
    850:        S_i((z-c)/h) = T_i(z),  c = (intv(j)+intv(j+1))) / 2,  h = (intv(j+1)-intv(j)) / 2,
    851:    with T_i(z) the Chebyshev polynomial of the first kind,
    852:        T_0(z) = 1, T_1(z) = z, and T_i(z) = 2*z*T_{i-1}(z) - T_{i-2}(z) for i>=2
    853:    tol is the tolerance; if the residual polynomial in z-norm is dropped by a factor lower
    854:        than tol, then stop the conjugate-residual iteration

    856:    output:
    857:    the return vector is x = x0 + s(A)*r0 with r0 = b - A*x0, such that ||(1-P(z))-z*s(z)||_w is minimized,
    858:    subject to that s(z) is a polynomial of degree up to niter, where P(z) is the base filter
    859:    in short, z*s(z) approximates 1-P(z)

    861:    note:
    862:    1. since z*s(z) approximates 1-P(z), P(0)==1 is expected; if P(0)!=1, one can translate P(z)
    863:       for example, if P(0)==0, one can use 1-P(z) as input instead of P(z)
    864:    2. typically, the base filter, defined by pp and intv, is from Hermite interpolation
    865:       in intervals [intv(j),intv(j+1)) for j=1,...,nintv, with nintv the number of intervals
    866:    3. a common application is to compute R(A)*b, where R(z) approximates 1-P(z)
    867:       in this case, one can set x0 = 0 and then the return vector is x = s(A)*b, where
    868:       z*s(z) approximates 1-P(z); therefore, A*x is the wanted R(A)*b
    869: */
    870: static PetscErrorCode FILTLAN_FilteredConjugateResidualMatrixPolynomialVectorProduct(Mat A,Vec b,Vec x,PetscReal *baseFilter,PetscInt nbase,PetscReal *intv,PetscInt nintv,PetscReal *intervalWeights,PetscInt niter,Vec *work)
    871: {
    872:   PetscInt       i,j,srpol,scpol,sarpol,sppol,sappol,ld;
    873:   PetscReal      rho,rho0,rho00,rho1,den,bet,alp,alp0,*cpol,*ppol,*rpol,*appol,*arpol,tol=0.0;
    874:   Vec            r=work[0],p=work[1],ap=work[2],w=work[3];
    875:   PetscScalar    alpha;

    877:   ld = niter+3;  /* leading dimension */
    878:   PetscCalloc5(ld*nintv,&ppol,ld*nintv,&rpol,ld*nintv,&cpol,ld*nintv,&appol,ld*nintv,&arpol);
    879:   /* initialize polynomial ppol to be 1 (i.e. multiplicative identity) in all intervals */
    880:   sppol = 2;
    881:   srpol = 2;
    882:   scpol = 2;
    883:   for (j=0;j<nintv;j++) {
    884:     ppol[j*ld] = 1.0;
    885:     rpol[j*ld] = 1.0;
    886:     cpol[j*ld] = 1.0;
    887:   }
    888:   /* ppol is the initial p-polynomial (corresponding to the A-conjugate vector p in CG)
    889:      rpol is the r-polynomial (corresponding to the residual vector r in CG)
    890:      cpol is the "corrected" residual polynomial */
    891:   sappol = 3;
    892:   sarpol = 3;
    893:   FILTLAN_PiecewisePolynomialInChebyshevBasisMultiplyX(ppol,sppol,ld,intv,nintv,appol,ld);
    894:   for (i=0;i<3;i++) for (j=0;j<nintv;j++) arpol[i+j*ld] = appol[i+j*ld];
    895:   rho00 = FILTLAN_PiecewisePolynomialInnerProductInChebyshevBasis(rpol,srpol,nintv,ld,arpol,sarpol,nintv,ld,intervalWeights);
    896:   rho = rho00;

    898:   /* corrected CR in vector space */
    899:   /* we assume x0 is always 0 */
    900:   VecSet(x,0.0);
    901:   VecCopy(b,r);     /* initial residual r = b-A*x0 */
    902:   VecCopy(r,p);     /* p = r */
    903:   MatMult(A,p,ap);  /* ap = A*p */

    905:   for (i=0;i<niter;i++) {
    906:     /* iteration in the polynomial space */
    907:     den = FILTLAN_PiecewisePolynomialInnerProductInChebyshevBasis(appol,sappol,nintv,ld,appol,sappol,nintv,ld,intervalWeights);
    908:     alp0 = rho/den;
    909:     rho1 = FILTLAN_PiecewisePolynomialInnerProductInChebyshevBasis(baseFilter,nbase,nintv,nbase,appol,sappol,nintv,ld,intervalWeights);
    910:     alp  = (rho-rho1)/den;
    911:     srpol++;
    912:     scpol++;
    913:     Mat_AXPY_BLAS(srpol,nintv,-alp0,appol,ld,1.0,rpol,ld);
    914:     Mat_AXPY_BLAS(scpol,nintv,-alp,appol,ld,1.0,cpol,ld);
    915:     sarpol++;
    916:     FILTLAN_PiecewisePolynomialInChebyshevBasisMultiplyX(rpol,srpol,ld,intv,nintv,arpol,ld);
    917:     rho0 = rho;
    918:     rho = FILTLAN_PiecewisePolynomialInnerProductInChebyshevBasis(rpol,srpol,nintv,ld,arpol,sarpol,nintv,ld,intervalWeights);

    920:     /* update x in the vector space */
    921:     alpha = alp;
    922:     VecAXPY(x,alpha,p);   /* x += alp*p */
    923:     if (rho < tol*rho00) break;

    925:     /* finish the iteration in the polynomial space */
    926:     bet = rho / rho0;
    927:     sppol++;
    928:     sappol++;
    929:     Mat_AXPY_BLAS(sppol,nintv,1.0,rpol,ld,bet,ppol,ld);
    930:     Mat_AXPY_BLAS(sappol,nintv,1.0,arpol,ld,bet,appol,ld);

    932:     /* finish the iteration in the vector space */
    933:     alpha = -alp0;
    934:     VecAXPY(r,alpha,ap);    /* r -= alp0*ap */
    935:     alpha = bet;
    936:     VecAYPX(p,alpha,r);     /* p = r + bet*p */
    937:     MatMult(A,r,w);         /* ap = A*r + bet*ap */
    938:     VecAYPX(ap,alpha,w);
    939:   }
    940:   PetscFree5(ppol,rpol,cpol,appol,arpol);
    941:   return 0;
    942: }

    944: /*
    945:    Gateway to FILTLAN for evaluating y=p(A)*x
    946: */
    947: PetscErrorCode MatMult_FILTLAN(Mat A,Vec x,Vec y)
    948: {
    949:   ST             st;
    950:   ST_FILTER      *ctx;
    951:   PetscInt       npoints;

    953:   MatShellGetContext(A,&st);
    954:   ctx = (ST_FILTER*)st->data;
    955:   npoints = (ctx->filterInfo->filterType == 2)? 6: 4;
    956:   FILTLAN_FilteredConjugateResidualMatrixPolynomialVectorProduct(ctx->T,x,y,ctx->baseFilter,2*ctx->baseDegree+2,ctx->intervals,npoints-1,ctx->opts->intervalWeights,ctx->polyDegree,st->work);
    957:   VecCopy(y,st->work[0]);
    958:   MatMult(ctx->T,st->work[0],y);
    959:   return 0;
    960: }

    962: /* Block version of FILTLAN_FilteredConjugateResidualMatrixPolynomialVectorProduct */
    963: static PetscErrorCode FILTLAN_FilteredConjugateResidualMatrixPolynomialVectorProductBlock(Mat A,Mat B,Mat C,PetscReal *baseFilter,PetscInt nbase,PetscReal *intv,PetscInt nintv,PetscReal *intervalWeights,PetscInt niter,Vec *work,Mat R,Mat P,Mat AP,Mat W)
    964: {
    965:   PetscInt       i,j,srpol,scpol,sarpol,sppol,sappol,ld;
    966:   PetscReal      rho,rho0,rho00,rho1,den,bet,alp,alp0,*cpol,*ppol,*rpol,*appol,*arpol,tol=0.0;
    967:   PetscScalar    alpha;

    969:   ld = niter+3;  /* leading dimension */
    970:   PetscCalloc5(ld*nintv,&ppol,ld*nintv,&rpol,ld*nintv,&cpol,ld*nintv,&appol,ld*nintv,&arpol);
    971:   /* initialize polynomial ppol to be 1 (i.e. multiplicative identity) in all intervals */
    972:   sppol = 2;
    973:   srpol = 2;
    974:   scpol = 2;
    975:   for (j=0;j<nintv;j++) {
    976:     ppol[j*ld] = 1.0;
    977:     rpol[j*ld] = 1.0;
    978:     cpol[j*ld] = 1.0;
    979:   }
    980:   /* ppol is the initial p-polynomial (corresponding to the A-conjugate vector p in CG)
    981:      rpol is the r-polynomial (corresponding to the residual vector r in CG)
    982:      cpol is the "corrected" residual polynomial */
    983:   sappol = 3;
    984:   sarpol = 3;
    985:   FILTLAN_PiecewisePolynomialInChebyshevBasisMultiplyX(ppol,sppol,ld,intv,nintv,appol,ld);
    986:   for (i=0;i<3;i++) for (j=0;j<nintv;j++) arpol[i+j*ld] = appol[i+j*ld];
    987:   rho00 = FILTLAN_PiecewisePolynomialInnerProductInChebyshevBasis(rpol,srpol,nintv,ld,arpol,sarpol,nintv,ld,intervalWeights);
    988:   rho = rho00;

    990:   /* corrected CR in vector space */
    991:   /* we assume x0 is always 0 */
    992:   MatZeroEntries(C);
    993:   MatCopy(B,R,SAME_NONZERO_PATTERN);     /* initial residual r = b-A*x0 */
    994:   MatCopy(R,P,SAME_NONZERO_PATTERN);     /* p = r */
    995:   MatMatMult(A,P,MAT_REUSE_MATRIX,PETSC_DEFAULT,&AP);  /* ap = A*p */

    997:   for (i=0;i<niter;i++) {
    998:     /* iteration in the polynomial space */
    999:     den = FILTLAN_PiecewisePolynomialInnerProductInChebyshevBasis(appol,sappol,nintv,ld,appol,sappol,nintv,ld,intervalWeights);
    1000:     alp0 = rho/den;
    1001:     rho1 = FILTLAN_PiecewisePolynomialInnerProductInChebyshevBasis(baseFilter,nbase,nintv,nbase,appol,sappol,nintv,ld,intervalWeights);
    1002:     alp  = (rho-rho1)/den;
    1003:     srpol++;
    1004:     scpol++;
    1005:     Mat_AXPY_BLAS(srpol,nintv,-alp0,appol,ld,1.0,rpol,ld);
    1006:     Mat_AXPY_BLAS(scpol,nintv,-alp,appol,ld,1.0,cpol,ld);
    1007:     sarpol++;
    1008:     FILTLAN_PiecewisePolynomialInChebyshevBasisMultiplyX(rpol,srpol,ld,intv,nintv,arpol,ld);
    1009:     rho0 = rho;
    1010:     rho = FILTLAN_PiecewisePolynomialInnerProductInChebyshevBasis(rpol,srpol,nintv,ld,arpol,sarpol,nintv,ld,intervalWeights);

    1012:     /* update x in the vector space */
    1013:     alpha = alp;
    1014:     MatAXPY(C,alpha,P,SAME_NONZERO_PATTERN);   /* x += alp*p */
    1015:     if (rho < tol*rho00) break;

    1017:     /* finish the iteration in the polynomial space */
    1018:     bet = rho / rho0;
    1019:     sppol++;
    1020:     sappol++;
    1021:     Mat_AXPY_BLAS(sppol,nintv,1.0,rpol,ld,bet,ppol,ld);
    1022:     Mat_AXPY_BLAS(sappol,nintv,1.0,arpol,ld,bet,appol,ld);

    1024:     /* finish the iteration in the vector space */
    1025:     alpha = -alp0;
    1026:     MatAXPY(R,alpha,AP,SAME_NONZERO_PATTERN);    /* r -= alp0*ap */
    1027:     alpha = bet;
    1028:     MatAYPX(P,alpha,R,SAME_NONZERO_PATTERN);     /* p = r + bet*p */
    1029:     MatMatMult(A,R,MAT_REUSE_MATRIX,PETSC_DEFAULT,&W);         /* ap = A*r + bet*ap */
    1030:     MatAYPX(AP,alpha,W,SAME_NONZERO_PATTERN);
    1031:   }
    1032:   PetscFree5(ppol,rpol,cpol,appol,arpol);
    1033:   return 0;
    1034: }

    1036: /*
    1037:    Gateway to FILTLAN for evaluating C=p(A)*B
    1038: */
    1039: PetscErrorCode MatMatMult_FILTLAN(Mat A,Mat B,Mat C,void *pctx)
    1040: {
    1041:   ST             st;
    1042:   ST_FILTER      *ctx;
    1043:   PetscInt       i,m1,m2,npoints;

    1045:   MatShellGetContext(A,&st);
    1046:   ctx = (ST_FILTER*)st->data;
    1047:   npoints = (ctx->filterInfo->filterType == 2)? 6: 4;
    1048:   if (ctx->nW) {  /* check if work matrices must be resized */
    1049:     MatGetSize(B,NULL,&m1);
    1050:     MatGetSize(ctx->W[0],NULL,&m2);
    1051:     if (m1!=m2) {
    1052:       MatDestroyMatrices(ctx->nW,&ctx->W);
    1053:       ctx->nW = 0;
    1054:     }
    1055:   }
    1056:   if (!ctx->nW) {  /* allocate work matrices */
    1057:     ctx->nW = 4;
    1058:     PetscMalloc1(ctx->nW,&ctx->W);
    1059:     for (i=0;i<ctx->nW;i++) MatDuplicate(B,MAT_DO_NOT_COPY_VALUES,&ctx->W[i]);
    1060:   }
    1061:   FILTLAN_FilteredConjugateResidualMatrixPolynomialVectorProductBlock(ctx->T,B,ctx->W[0],ctx->baseFilter,2*ctx->baseDegree+2,ctx->intervals,npoints-1,ctx->opts->intervalWeights,ctx->polyDegree,st->work,C,ctx->W[1],ctx->W[2],ctx->W[3]);
    1062:   MatMatMult(ctx->T,ctx->W[0],MAT_REUSE_MATRIX,PETSC_DEFAULT,&C);
    1063:   return 0;
    1064: }

    1066: /*
    1067:    FILTLAN function PolynomialFilterInterface::setFilter

    1069:    Creates the shifted (and scaled) matrix and the base filter P(z).
    1070:    M is a shell matrix whose MatMult() applies the filter.
    1071: */
    1072: PetscErrorCode STFilter_FILTLAN_setFilter(ST st,Mat *G)
    1073: {
    1074:   ST_FILTER      *ctx = (ST_FILTER*)st->data;
    1075:   PetscInt       i,npoints,n,m,N,M;
    1076:   PetscReal      frame2[4];
    1077:   PetscScalar    alpha;
    1078:   const PetscInt HighLowFlags[5] = { 1, -1, 0, -1, 1 };

    1080:   if (ctx->frame[0] == ctx->frame[1]) {  /* low pass filter, convert it to high pass filter */
    1081:     /* T = frame[3]*eye(n) - A */
    1082:     MatDestroy(&ctx->T);
    1083:     MatDuplicate(st->A[0],MAT_COPY_VALUES,&ctx->T);
    1084:     MatScale(ctx->T,-1.0);
    1085:     alpha = ctx->frame[3];
    1086:     MatShift(ctx->T,alpha);
    1087:     for (i=0;i<4;i++) frame2[i] = ctx->frame[3] - ctx->frame[3-i];
    1088:   } else {  /* it can be a mid-pass filter or a high-pass filter */
    1089:     if (ctx->frame[0] == 0.0) {
    1090:       PetscObjectReference((PetscObject)st->A[0]);
    1091:       MatDestroy(&ctx->T);
    1092:       ctx->T = st->A[0];
    1093:       for (i=0;i<4;i++) frame2[i] = ctx->frame[i];
    1094:     } else {
    1095:       /* T = A - frame[0]*eye(n) */
    1096:       MatDestroy(&ctx->T);
    1097:       MatDuplicate(st->A[0],MAT_COPY_VALUES,&ctx->T);
    1098:       alpha = -ctx->frame[0];
    1099:       MatShift(ctx->T,alpha);
    1100:       for (i=0;i<4;i++) frame2[i] = ctx->frame[i] - ctx->frame[0];
    1101:     }
    1102:   }

    1104:   /* no need to recompute filter if the parameters did not change */
    1105:   if (st->state==ST_STATE_INITIAL || ctx->filtch) {
    1106:     FILTLAN_GetIntervals(ctx->intervals,frame2,ctx->polyDegree,ctx->baseDegree,ctx->opts,ctx->filterInfo);
    1107:     /* translate the intervals back */
    1108:     if (ctx->frame[0] == ctx->frame[1]) {  /* low pass filter, convert it to high pass filter */
    1109:       for (i=0;i<4;i++) ctx->intervals2[i] = ctx->frame[3] - ctx->intervals[3-i];
    1110:     } else {  /* it can be a mid-pass filter or a high-pass filter */
    1111:       if (ctx->frame[0] == 0.0) {
    1112:         for (i=0;i<6;i++) ctx->intervals2[i] = ctx->intervals[i];
    1113:       } else {
    1114:         for (i=0;i<6;i++) ctx->intervals2[i] = ctx->intervals[i] + ctx->frame[0];
    1115:       }
    1116:     }

    1118:     npoints = (ctx->filterInfo->filterType == 2)? 6: 4;
    1119:     PetscFree(ctx->baseFilter);
    1120:     PetscMalloc1((2*ctx->baseDegree+2)*(npoints-1),&ctx->baseFilter);
    1121:     FILTLAN_HermiteBaseFilterInChebyshevBasis(ctx->baseFilter,ctx->intervals,npoints,HighLowFlags,ctx->baseDegree);
    1122:     PetscInfo(st,"Computed value of yLimit = %g\n",(double)ctx->filterInfo->yLimit);
    1123:   }
    1124:   ctx->filtch = PETSC_FALSE;

    1126:   /* create shell matrix*/
    1127:   if (!*G) {
    1128:     MatGetSize(ctx->T,&N,&M);
    1129:     MatGetLocalSize(ctx->T,&n,&m);
    1130:     MatCreateShell(PetscObjectComm((PetscObject)st),n,m,N,M,st,G);
    1131:     MatShellSetOperation(*G,MATOP_MULT,(void(*)(void))MatMult_FILTLAN);
    1132:     MatShellSetMatProductOperation(*G,MATPRODUCT_AB,NULL,MatMatMult_FILTLAN,NULL,MATDENSE,MATDENSE);
    1133:     MatShellSetMatProductOperation(*G,MATPRODUCT_AB,NULL,MatMatMult_FILTLAN,NULL,MATDENSECUDA,MATDENSECUDA);
    1134:   }
    1135:   return 0;
    1136: }