#include <petsc/private/kspimpl.h> /*I "petscksp.h" I*/
#include <petsc/private/matimpl.h>
#include <petscblaslapack.h>
static PetscBool  cited      = PETSC_FALSE;
static const char citation[] = "@phdthesis{zampini2010non,\n"
                               "  title={Non-overlapping Domain Decomposition Methods for Cardiac Reaction-Diffusion Models and Applications},\n"
                               "  author={Zampini, S},\n"
                               "  year={2010},\n"
                               "  school={PhD thesis, Universita degli Studi di Milano}\n"
                               "}\n";

typedef struct {
  PetscInt      maxn;  /* maximum number of snapshots */
  PetscInt      n;     /* number of active snapshots */
  PetscInt      curr;  /* current tip of snapshots set */
  Vec          *xsnap; /* snapshots */
  Vec          *bsnap; /* rhs snapshots */
  Vec          *work;  /* parallel work vectors */
  PetscScalar  *dots_iallreduce;
  MPI_Request   req_iallreduce;
  PetscInt      ndots_iallreduce; /* if we have iallreduce we can hide the VecMDot communications */
  PetscReal     tol;              /* relative tolerance to retain eigenvalues */
  PetscBool     Aspd;             /* if true, uses the SPD operator as inner product */
  PetscScalar  *corr;             /* correlation matrix */
  PetscReal    *eigs;             /* eigenvalues */
  PetscScalar  *eigv;             /* eigenvectors */
  PetscBLASInt  nen;              /* dimension of lower dimensional system */
  PetscInt      st;               /* first eigenvector of correlation matrix to be retained */
  PetscBLASInt *iwork;            /* integer work vector */
  PetscScalar  *yhay;             /* Y^H * A * Y */
  PetscScalar  *low;              /* lower dimensional linear system */
#if defined(PETSC_USE_COMPLEX)
  PetscReal *rwork;
#endif
  PetscBLASInt lwork;
  PetscScalar *swork;
  PetscBool    monitor;
} KSPGuessPOD;

static PetscErrorCode KSPGuessReset_POD(KSPGuess guess)
{
  KSPGuessPOD *pod  = (KSPGuessPOD *)guess->data;
  PetscLayout  Alay = NULL, vlay = NULL;
  PetscBool    cong;

  PetscFunctionBegin;
  pod->nen  = 0;
  pod->n    = 0;
  pod->curr = 0;
  /* need to wait for completion of outstanding requests */
  if (pod->ndots_iallreduce) PetscCallMPI(MPI_Wait(&pod->req_iallreduce, MPI_STATUS_IGNORE));
  pod->ndots_iallreduce = 0;
  /* destroy vectors if the size of the linear system has changed */
  if (guess->A) PetscCall(MatGetLayouts(guess->A, &Alay, NULL));
  if (pod->xsnap) PetscCall(VecGetLayout(pod->xsnap[0], &vlay));
  cong = PETSC_FALSE;
  if (vlay && Alay) PetscCall(PetscLayoutCompare(Alay, vlay, &cong));
  if (!cong) {
    PetscCall(VecDestroyVecs(pod->maxn, &pod->xsnap));
    PetscCall(VecDestroyVecs(pod->maxn, &pod->bsnap));
    PetscCall(VecDestroyVecs(1, &pod->work));
  }
  PetscFunctionReturn(PETSC_SUCCESS);
}

static PetscErrorCode KSPGuessSetUp_POD(KSPGuess guess)
{
  KSPGuessPOD *pod = (KSPGuessPOD *)guess->data;

  PetscFunctionBegin;
  if (!pod->corr) {
    PetscScalar  sdummy;
    PetscReal    rdummy = 0;
    PetscBLASInt bN, lierr, idummy;

    PetscCall(PetscCalloc6(pod->maxn * pod->maxn, &pod->corr, pod->maxn, &pod->eigs, pod->maxn * pod->maxn, &pod->eigv, 6 * pod->maxn, &pod->iwork, pod->maxn * pod->maxn, &pod->yhay, pod->maxn * pod->maxn, &pod->low));
#if defined(PETSC_USE_COMPLEX)
    PetscCall(PetscMalloc1(7 * pod->maxn, &pod->rwork));
#endif
#if defined(PETSC_HAVE_MPI_NONBLOCKING_COLLECTIVES)
    PetscCall(PetscMalloc1(3 * pod->maxn, &pod->dots_iallreduce));
#endif
    pod->lwork = -1;
    PetscCall(PetscBLASIntCast(pod->maxn, &bN));
#if !defined(PETSC_USE_COMPLEX)
    PetscCallBLAS("LAPACKsyevx", LAPACKsyevx_("V", "A", "L", &bN, pod->corr, &bN, &rdummy, &rdummy, &idummy, &idummy, &rdummy, &idummy, pod->eigs, pod->eigv, &bN, &sdummy, &pod->lwork, pod->iwork, pod->iwork + 5 * bN, &lierr));
#else
    PetscCallBLAS("LAPACKsyevx", LAPACKsyevx_("V", "A", "L", &bN, pod->corr, &bN, &rdummy, &rdummy, &idummy, &idummy, &rdummy, &idummy, pod->eigs, pod->eigv, &bN, &sdummy, &pod->lwork, pod->rwork, pod->iwork, pod->iwork + 5 * bN, &lierr));
#endif
    PetscCheck(!lierr, PETSC_COMM_SELF, PETSC_ERR_LIB, "Error in query to SYEV Lapack routine %d", (int)lierr);
    PetscCall(PetscBLASIntCast((PetscInt)PetscRealPart(sdummy), &pod->lwork));
    PetscCall(PetscMalloc1(pod->lwork + PetscMax(bN * bN, 6 * bN), &pod->swork));
  }
  /* work vectors are sequential, we explicitly use MPI_Allreduce */
  if (!pod->xsnap) {
    Vec *v, vseq;

    PetscCall(KSPCreateVecs(guess->ksp, 1, &v, 0, NULL));
    PetscCall(VecCreateLocalVector(v[0], &vseq));
    PetscCall(VecDestroyVecs(1, &v));
    PetscCall(VecDuplicateVecs(vseq, pod->maxn, &pod->xsnap));
    PetscCall(VecDestroy(&vseq));
  }
  if (!pod->bsnap) {
    Vec *v, vseq;

    PetscCall(KSPCreateVecs(guess->ksp, 0, NULL, 1, &v));
    PetscCall(VecCreateLocalVector(v[0], &vseq));
    PetscCall(VecDestroyVecs(1, &v));
    PetscCall(VecDuplicateVecs(vseq, pod->maxn, &pod->bsnap));
    PetscCall(VecDestroy(&vseq));
  }
  if (!pod->work) PetscCall(KSPCreateVecs(guess->ksp, 1, &pod->work, 0, NULL));
  PetscFunctionReturn(PETSC_SUCCESS);
}

static PetscErrorCode KSPGuessDestroy_POD(KSPGuess guess)
{
  KSPGuessPOD *pod = (KSPGuessPOD *)guess->data;

  PetscFunctionBegin;
  PetscCall(PetscFree6(pod->corr, pod->eigs, pod->eigv, pod->iwork, pod->yhay, pod->low));
#if defined(PETSC_USE_COMPLEX)
  PetscCall(PetscFree(pod->rwork));
#endif
  /* need to wait for completion before destroying dots_iallreduce */
  if (pod->ndots_iallreduce) PetscCallMPI(MPI_Wait(&pod->req_iallreduce, MPI_STATUS_IGNORE));
  PetscCall(PetscFree(pod->dots_iallreduce));
  PetscCall(PetscFree(pod->swork));
  PetscCall(VecDestroyVecs(pod->maxn, &pod->bsnap));
  PetscCall(VecDestroyVecs(pod->maxn, &pod->xsnap));
  PetscCall(VecDestroyVecs(1, &pod->work));
  PetscCall(PetscFree(pod));
  PetscFunctionReturn(PETSC_SUCCESS);
}

static PetscErrorCode KSPGuessUpdate_POD(KSPGuess, Vec, Vec);

static PetscErrorCode KSPGuessFormGuess_POD(KSPGuess guess, Vec b, Vec x)
{
  KSPGuessPOD *pod = (KSPGuessPOD *)guess->data;
  PetscScalar  one = 1, zero = 0;
  PetscBLASInt bN, ione      = 1, bNen, lierr;
  PetscInt     i;

  PetscFunctionBegin;
  PetscCall(PetscCitationsRegister(citation, &cited));
  if (pod->ndots_iallreduce) { /* complete communication and project the linear system */
    PetscCall(KSPGuessUpdate_POD(guess, NULL, NULL));
  }
  if (!pod->nen) PetscFunctionReturn(PETSC_SUCCESS);
  /* b_low = S * V^T * X^T * b */
  PetscCall(VecGetLocalVectorRead(b, pod->bsnap[pod->curr]));
  PetscCall(VecMDot(pod->bsnap[pod->curr], pod->n, pod->xsnap, pod->swork));
  PetscCall(VecRestoreLocalVectorRead(b, pod->bsnap[pod->curr]));
  PetscCallMPI(MPIU_Allreduce(pod->swork, pod->swork + pod->n, (PetscMPIInt)pod->n, MPIU_SCALAR, MPIU_SUM, PetscObjectComm((PetscObject)guess)));
  PetscCall(PetscBLASIntCast(pod->n, &bN));
  PetscCall(PetscBLASIntCast(pod->nen, &bNen));
  PetscCallBLAS("BLASgemv", BLASgemv_("T", &bN, &bNen, &one, pod->eigv + pod->st * pod->n, &bN, pod->swork + pod->n, &ione, &zero, pod->swork, &ione));
  if (pod->monitor) {
    PetscCall(PetscPrintf(PetscObjectComm((PetscObject)guess), "  KSPGuessPOD alphas = "));
    for (i = 0; i < pod->nen; i++) {
#if defined(PETSC_USE_COMPLEX)
      PetscCall(PetscPrintf(PetscObjectComm((PetscObject)guess), "%g + %g i", (double)PetscRealPart(pod->swork[i]), (double)PetscImaginaryPart(pod->swork[i])));
#else
      PetscCall(PetscPrintf(PetscObjectComm((PetscObject)guess), "%g ", (double)pod->swork[i]));
#endif
    }
    PetscCall(PetscPrintf(PetscObjectComm((PetscObject)guess), "\n"));
  }
  /* A_low x_low = b_low */
  if (!pod->Aspd) { /* A is spd -> LOW = Identity */
    KSP       pksp = guess->ksp;
    PetscBool tsolve, symm, set;

    if (pod->monitor) {
      PetscMPIInt rank;
      Mat         L;

      PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)guess), &rank));
      if (rank == 0) {
        PetscCall(PetscPrintf(PetscObjectComm((PetscObject)guess), "  L = "));
        PetscCall(MatCreateSeqDense(PETSC_COMM_SELF, pod->nen, pod->nen, pod->low, &L));
        PetscCall(MatView(L, NULL));
        PetscCall(MatDestroy(&L));
      }
    }
    PetscCall(MatIsSymmetricKnown(guess->A, &set, &symm));
    tsolve = (set && symm) ? PETSC_FALSE : pksp->transpose_solve;
    PetscCallBLAS("LAPACKgetrf", LAPACKgetrf_(&bNen, &bNen, pod->low, &bNen, pod->iwork, &lierr));
    PetscCheck(!lierr, PETSC_COMM_SELF, PETSC_ERR_LIB, "Error in GETRF Lapack routine %d", (int)lierr);
    PetscCallBLAS("LAPACKgetrs", LAPACKgetrs_(tsolve ? "T" : "N", &bNen, &ione, pod->low, &bNen, pod->iwork, pod->swork, &bNen, &lierr));
    PetscCheck(!lierr, PETSC_COMM_SELF, PETSC_ERR_LIB, "Error in GETRS Lapack routine %d", (int)lierr);
  }
  /* x = X * V * S * x_low */
  PetscCallBLAS("BLASgemv", BLASgemv_("N", &bN, &bNen, &one, pod->eigv + pod->st * pod->n, &bN, pod->swork, &ione, &zero, pod->swork + pod->n, &ione));
  if (pod->monitor) {
    PetscCall(PetscPrintf(PetscObjectComm((PetscObject)guess), "  KSPGuessPOD sol = "));
    for (i = 0; i < pod->nen; i++) {
#if defined(PETSC_USE_COMPLEX)
      PetscCall(PetscPrintf(PetscObjectComm((PetscObject)guess), "%g + %g i", (double)PetscRealPart(pod->swork[i + pod->n]), (double)PetscImaginaryPart(pod->swork[i + pod->n])));
#else
      PetscCall(PetscPrintf(PetscObjectComm((PetscObject)guess), "%g ", (double)pod->swork[i + pod->n]));
#endif
    }
    PetscCall(PetscPrintf(PetscObjectComm((PetscObject)guess), "\n"));
  }
  PetscCall(VecGetLocalVector(x, pod->bsnap[pod->curr]));
  PetscCall(VecSet(pod->bsnap[pod->curr], 0));
  PetscCall(VecMAXPY(pod->bsnap[pod->curr], pod->n, pod->swork + pod->n, pod->xsnap));
  PetscCall(VecRestoreLocalVector(x, pod->bsnap[pod->curr]));
  PetscFunctionReturn(PETSC_SUCCESS);
}

static PetscErrorCode KSPGuessUpdate_POD(KSPGuess guess, Vec b, Vec x)
{
  KSPGuessPOD *pod = (KSPGuessPOD *)guess->data;
  PetscScalar  one = 1, zero = 0;
  PetscReal    toten, parten, reps = 0; /* dlamch? */
  PetscBLASInt bN, lierr, idummy;
  PetscInt     i;

  PetscFunctionBegin;
  if (pod->ndots_iallreduce) goto complete_request;
  pod->n = pod->n < pod->maxn ? pod->n + 1 : pod->maxn;
  PetscCall(VecCopy(x, pod->xsnap[pod->curr]));
  PetscCall(KSP_MatMult(guess->ksp, guess->A, x, pod->work[0]));
  PetscCall(VecCopy(pod->work[0], pod->bsnap[pod->curr]));
  if (pod->Aspd) {
    PetscCall(VecMDot(pod->xsnap[pod->curr], pod->n, pod->bsnap, pod->swork));
#if !defined(PETSC_HAVE_MPI_NONBLOCKING_COLLECTIVES)
    PetscCallMPI(MPIU_Allreduce(pod->swork, pod->swork + 3 * pod->n, pod->n, MPIU_SCALAR, MPIU_SUM, PetscObjectComm((PetscObject)guess)));
#else
    PetscCallMPI(MPI_Iallreduce(pod->swork, pod->dots_iallreduce, (PetscMPIInt)pod->n, MPIU_SCALAR, MPIU_SUM, PetscObjectComm((PetscObject)guess), &pod->req_iallreduce));
    pod->ndots_iallreduce = 1;
#endif
  } else {
    PetscInt  off;
    PetscBool set, herm;

#if defined(PETSC_USE_COMPLEX)
    PetscCall(MatIsHermitianKnown(guess->A, &set, &herm));
#else
    PetscCall(MatIsSymmetricKnown(guess->A, &set, &herm));
#endif
    off = (guess->ksp->transpose_solve && (!set || !herm)) ? 2 * pod->n : pod->n;

    /* TODO: we may want to use a user-defined dot for the correlation matrix */
    PetscCall(VecMDot(pod->xsnap[pod->curr], pod->n, pod->xsnap, pod->swork));
    PetscCall(VecMDot(pod->bsnap[pod->curr], pod->n, pod->xsnap, pod->swork + off));
    if (!set || !herm) {
      off = (off == pod->n) ? 2 * pod->n : pod->n;
      PetscCall(VecMDot(pod->xsnap[pod->curr], pod->n, pod->bsnap, pod->swork + off));
#if !defined(PETSC_HAVE_MPI_NONBLOCKING_COLLECTIVES)
      PetscCallMPI(MPIU_Allreduce(pod->swork, pod->swork + 3 * pod->n, (PetscMPIInt)(3 * pod->n), MPIU_SCALAR, MPIU_SUM, PetscObjectComm((PetscObject)guess)));
#else
      PetscCallMPI(MPI_Iallreduce(pod->swork, pod->dots_iallreduce, (PetscMPIInt)(3 * pod->n), MPIU_SCALAR, MPIU_SUM, PetscObjectComm((PetscObject)guess), &pod->req_iallreduce));
      pod->ndots_iallreduce = 3;
#endif
    } else {
#if !defined(PETSC_HAVE_MPI_NONBLOCKING_COLLECTIVES)
      PetscCallMPI(MPIU_Allreduce(pod->swork, pod->swork + 3 * pod->n, (PetscMPIInt)(2 * pod->n), MPIU_SCALAR, MPIU_SUM, PetscObjectComm((PetscObject)guess)));
      for (i = 0; i < pod->n; i++) pod->swork[5 * pod->n + i] = pod->swork[4 * pod->n + i];
#else
      PetscCallMPI(MPI_Iallreduce(pod->swork, pod->dots_iallreduce, (PetscMPIInt)(2 * pod->n), MPIU_SCALAR, MPIU_SUM, PetscObjectComm((PetscObject)guess), &pod->req_iallreduce));
      pod->ndots_iallreduce = 2;
#endif
    }
  }
  if (pod->ndots_iallreduce) PetscFunctionReturn(PETSC_SUCCESS);

complete_request:
  if (pod->ndots_iallreduce) {
    PetscCallMPI(MPI_Wait(&pod->req_iallreduce, MPI_STATUS_IGNORE));
    switch (pod->ndots_iallreduce) {
    case 3:
      for (i = 0; i < pod->n; i++) pod->swork[3 * pod->n + i] = pod->dots_iallreduce[i];
      for (i = 0; i < pod->n; i++) pod->swork[4 * pod->n + i] = pod->dots_iallreduce[pod->n + i];
      for (i = 0; i < pod->n; i++) pod->swork[5 * pod->n + i] = pod->dots_iallreduce[2 * pod->n + i];
      break;
    case 2:
      for (i = 0; i < pod->n; i++) pod->swork[3 * pod->n + i] = pod->dots_iallreduce[i];
      for (i = 0; i < pod->n; i++) pod->swork[4 * pod->n + i] = pod->dots_iallreduce[pod->n + i];
      for (i = 0; i < pod->n; i++) pod->swork[5 * pod->n + i] = pod->dots_iallreduce[pod->n + i];
      break;
    case 1:
      for (i = 0; i < pod->n; i++) pod->swork[3 * pod->n + i] = pod->dots_iallreduce[i];
      break;
    default:
      SETERRQ(PetscObjectComm((PetscObject)guess), PETSC_ERR_PLIB, "Invalid number of outstanding dots operations: %" PetscInt_FMT, pod->ndots_iallreduce);
    }
  }
  pod->ndots_iallreduce = 0;

  /* correlation matrix and Y^H A Y (Galerkin) */
  for (i = 0; i < pod->n; i++) {
    pod->corr[pod->curr * pod->maxn + i] = pod->swork[3 * pod->n + i];
    pod->corr[i * pod->maxn + pod->curr] = PetscConj(pod->swork[3 * pod->n + i]);
    if (!pod->Aspd) {
      pod->yhay[pod->curr * pod->maxn + i] = pod->swork[4 * pod->n + i];
      pod->yhay[i * pod->maxn + pod->curr] = PetscConj(pod->swork[5 * pod->n + i]);
    }
  }
  /* syevx changes the input matrix */
  for (i = 0; i < pod->n; i++) {
    PetscInt j;
    for (j = i; j < pod->n; j++) pod->swork[i * pod->n + j] = pod->corr[i * pod->maxn + j];
  }
  PetscCall(PetscBLASIntCast(pod->n, &bN));
#if !defined(PETSC_USE_COMPLEX)
  PetscCallBLAS("LAPACKsyevx", LAPACKsyevx_("V", "A", "L", &bN, pod->swork, &bN, &reps, &reps, &idummy, &idummy, &reps, &idummy, pod->eigs, pod->eigv, &bN, pod->swork + bN * bN, &pod->lwork, pod->iwork, pod->iwork + 5 * bN, &lierr));
#else
  PetscCallBLAS("LAPACKsyevx", LAPACKsyevx_("V", "A", "L", &bN, pod->swork, &bN, &reps, &reps, &idummy, &idummy, &reps, &idummy, pod->eigs, pod->eigv, &bN, pod->swork + bN * bN, &pod->lwork, pod->rwork, pod->iwork, pod->iwork + 5 * bN, &lierr));
#endif
  PetscCheck(lierr >= 0, PETSC_COMM_SELF, PETSC_ERR_LIB, "Error in SYEV Lapack routine: illegal argument %d", -(int)lierr);
  PetscCheck(!lierr, PETSC_COMM_SELF, PETSC_ERR_LIB, "Error in SYEV Lapack routine: %d eigenvectors failed to converge", (int)lierr);

  /* dimension of lower dimensional system */
  pod->st = -1;
  for (i = 0, toten = 0; i < pod->n; i++) {
    pod->eigs[i] = PetscMax(pod->eigs[i], 0.0);
    toten += pod->eigs[i];
    if (!pod->eigs[i]) pod->st = i;
  }
  pod->nen = 0;
  for (i = pod->n - 1, parten = 0; i > pod->st && toten > 0; i--) {
    pod->nen++;
    parten += pod->eigs[i];
    if (parten + toten * pod->tol >= toten) break;
  }
  pod->st = pod->n - pod->nen;

  /* Compute eigv = V * S */
  for (i = pod->st; i < pod->n; i++) {
    const PetscReal v  = 1.0 / PetscSqrtReal(pod->eigs[i]);
    const PetscInt  st = pod->n * i;
    PetscInt        j;

    for (j = 0; j < pod->n; j++) pod->eigv[st + j] *= v;
  }

  /* compute S * V^T * X^T * A * X * V * S if needed */
  if (pod->nen && !pod->Aspd) {
    PetscBLASInt bNen, bMaxN;
    PetscInt     st = pod->st * pod->n;
    PetscCall(PetscBLASIntCast(pod->nen, &bNen));
    PetscCall(PetscBLASIntCast(pod->maxn, &bMaxN));
    PetscCallBLAS("BLASgemm", BLASgemm_("T", "N", &bNen, &bN, &bN, &one, pod->eigv + st, &bN, pod->yhay, &bMaxN, &zero, pod->swork, &bNen));
    PetscCallBLAS("BLASgemm", BLASgemm_("N", "N", &bNen, &bNen, &bN, &one, pod->swork, &bNen, pod->eigv + st, &bN, &zero, pod->low, &bNen));
  }

  if (pod->monitor) {
    PetscMPIInt rank;
    Mat         C;

    PetscCallMPI(MPI_Comm_rank(PetscObjectComm((PetscObject)guess), &rank));
    if (rank == 0) {
      PetscCall(PetscPrintf(PetscObjectComm((PetscObject)guess), "  C = "));
      PetscCall(MatCreateSeqDense(PETSC_COMM_SELF, pod->n, pod->n, pod->corr, &C));
      PetscCall(MatDenseSetLDA(C, pod->maxn));
      PetscCall(MatView(C, NULL));
      PetscCall(MatDestroy(&C));
      PetscCall(PetscPrintf(PetscObjectComm((PetscObject)guess), "  YHAY = "));
      PetscCall(MatCreateSeqDense(PETSC_COMM_SELF, pod->n, pod->n, pod->yhay, &C));
      PetscCall(MatDenseSetLDA(C, pod->maxn));
      PetscCall(MatView(C, NULL));
      PetscCall(MatDestroy(&C));
    }
    PetscCall(PetscPrintf(PetscObjectComm((PetscObject)guess), "  KSPGuessPOD: basis %" PetscBLASInt_FMT ", energy fractions = ", pod->nen));
    for (i = pod->n - 1; i >= 0; i--) PetscCall(PetscPrintf(PetscObjectComm((PetscObject)guess), "%1.6e (%d) ", (double)(pod->eigs[i] / toten), i >= pod->st ? 1 : 0));
    PetscCall(PetscPrintf(PetscObjectComm((PetscObject)guess), "\n"));
    if (PetscDefined(USE_DEBUG)) {
      for (i = 0; i < pod->n; i++) {
        Vec          v;
        PetscInt     j;
        PetscBLASInt bNen, ione = 1;

        PetscCall(VecDuplicate(pod->xsnap[i], &v));
        PetscCall(VecCopy(pod->xsnap[i], v));
        PetscCall(PetscBLASIntCast(pod->nen, &bNen));
        PetscCallBLAS("BLASgemv", BLASgemv_("T", &bN, &bNen, &one, pod->eigv + pod->st * pod->n, &bN, pod->corr + pod->maxn * i, &ione, &zero, pod->swork, &ione));
        PetscCallBLAS("BLASgemv", BLASgemv_("N", &bN, &bNen, &one, pod->eigv + pod->st * pod->n, &bN, pod->swork, &ione, &zero, pod->swork + pod->n, &ione));
        for (j = 0; j < pod->n; j++) pod->swork[j] = -pod->swork[pod->n + j];
        PetscCall(VecMAXPY(v, pod->n, pod->swork, pod->xsnap));
        PetscCall(VecDot(v, v, pod->swork));
        PetscCallMPI(MPIU_Allreduce(pod->swork, pod->swork + 1, 1, MPIU_SCALAR, MPIU_SUM, PetscObjectComm((PetscObject)guess)));
        PetscCall(PetscPrintf(PetscObjectComm((PetscObject)guess), "  Error projection %" PetscInt_FMT ": %g (expected lower than %g)\n", i, (double)PetscRealPart(pod->swork[1]), (double)(toten - parten)));
        PetscCall(VecDestroy(&v));
      }
    }
  }
  /* new tip */
  pod->curr = (pod->curr + 1) % pod->maxn;
  PetscFunctionReturn(PETSC_SUCCESS);
}

static PetscErrorCode KSPGuessSetFromOptions_POD(KSPGuess guess)
{
  KSPGuessPOD *pod = (KSPGuessPOD *)guess->data;

  PetscFunctionBegin;
  PetscOptionsBegin(PetscObjectComm((PetscObject)guess), ((PetscObject)guess)->prefix, "POD initial guess options", "KSPGuess");
  PetscCall(PetscOptionsInt("-ksp_guess_pod_size", "Number of snapshots", NULL, pod->maxn, &pod->maxn, NULL));
  PetscCall(PetscOptionsBool("-ksp_guess_pod_monitor", "Monitor initial guess generator", NULL, pod->monitor, &pod->monitor, NULL));
  PetscCall(PetscOptionsReal("-ksp_guess_pod_tol", "Tolerance to retain eigenvectors", "KSPGuessSetTolerance", pod->tol, &pod->tol, NULL));
  PetscCall(PetscOptionsBool("-ksp_guess_pod_Ainner", "Use the operator as inner product (must be SPD)", NULL, pod->Aspd, &pod->Aspd, NULL));
  PetscOptionsEnd();
  PetscFunctionReturn(PETSC_SUCCESS);
}

static PetscErrorCode KSPGuessSetTolerance_POD(KSPGuess guess, PetscReal tol)
{
  KSPGuessPOD *pod = (KSPGuessPOD *)guess->data;

  PetscFunctionBegin;
  pod->tol = tol;
  PetscFunctionReturn(PETSC_SUCCESS);
}

static PetscErrorCode KSPGuessView_POD(KSPGuess guess, PetscViewer viewer)
{
  KSPGuessPOD *pod = (KSPGuessPOD *)guess->data;
  PetscBool    isascii;

  PetscFunctionBegin;
  PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
  if (isascii) PetscCall(PetscViewerASCIIPrintf(viewer, "Max size %" PetscInt_FMT ", tolerance %g, Ainner %d\n", pod->maxn, (double)pod->tol, pod->Aspd));
  PetscFunctionReturn(PETSC_SUCCESS);
}

/*MC
    KSPGUESSPOD - Implements a proper orthogonal decomposition based Galerkin scheme for repeated linear system solves.

  Options Database Keys:
+  -ksp_guess_pod_size <size> - Number of snapshots
.  -ksp_guess_pod_monitor <true or false> - Monitor initial guess generator
.  -ksp_guess_pod_tol <tol> - Tolerance to retain eigenvectors
-  -ksp_guess_pod_Ainner <true> - Use the operator as inner product (must be SPD)

  Level: intermediate

  Note:
  The initial guess is obtained by solving a small and dense linear system, obtained by Galerkin projection on a lower dimensional space generated by the previous solutions as presented in {cite}`volkwein2013proper`.

.seealso: [](ch_ksp), `KSPGuess`, `KSPGuessType`, `KSPGuessCreate()`, `KSPSetGuess()`, `KSPGetGuess()`
M*/
PetscErrorCode KSPGuessCreate_POD(KSPGuess guess)
{
  KSPGuessPOD *pod;

  PetscFunctionBegin;
  PetscCall(PetscNew(&pod));
  pod->maxn   = 10;
  pod->tol    = PETSC_MACHINE_EPSILON;
  guess->data = pod;

  guess->ops->setfromoptions = KSPGuessSetFromOptions_POD;
  guess->ops->destroy        = KSPGuessDestroy_POD;
  guess->ops->settolerance   = KSPGuessSetTolerance_POD;
  guess->ops->setup          = KSPGuessSetUp_POD;
  guess->ops->view           = KSPGuessView_POD;
  guess->ops->reset          = KSPGuessReset_POD;
  guess->ops->update         = KSPGuessUpdate_POD;
  guess->ops->formguess      = KSPGuessFormGuess_POD;
  PetscFunctionReturn(PETSC_SUCCESS);
}
