dlansy function

Implementation

double dlansy(
  final String NORM,
  final String UPLO,
  final int N,
  final Matrix<double> A_,
  final int LDA,
  final Array<double> WORK_,
) {
  final A = A_.having(ld: LDA);
  final WORK = WORK_.having();
  const ONE = 1.0, ZERO = 0.0;
  int I, J;
  double ABSA, VALUE = 0;
  final SCALE = Box(0.0), SUM = Box(0.0);

  if (N == 0) {
    VALUE = ZERO;
  } else if (lsame(NORM, 'M')) {
    // Find max(abs(A(i,j))).

    VALUE = ZERO;
    if (lsame(UPLO, 'U')) {
      for (J = 1; J <= N; J++) {
        for (I = 1; I <= J; I++) {
          SUM.value = A[I][J].abs();
          if (VALUE < SUM.value || disnan(SUM.value)) VALUE = SUM.value;
        }
      }
    } else {
      for (J = 1; J <= N; J++) {
        for (I = J; I <= N; I++) {
          SUM.value = A[I][J].abs();
          if (VALUE < SUM.value || disnan(SUM.value)) VALUE = SUM.value;
        }
      }
    }
  } else if ((lsame(NORM, 'I')) || (lsame(NORM, 'O')) || (NORM == '1')) {
    // Find normI(A) ( = norm1(A), since A is symmetric).

    VALUE = ZERO;
    if (lsame(UPLO, 'U')) {
      for (J = 1; J <= N; J++) {
        SUM.value = ZERO;
        for (I = 1; I <= J - 1; I++) {
          ABSA = A[I][J].abs();
          SUM.value += ABSA;
          WORK[I] += ABSA;
        }
        WORK[J] = SUM.value + A[J][J].abs();
      }
      for (I = 1; I <= N; I++) {
        SUM.value = WORK[I];
        if (VALUE < SUM.value || disnan(SUM.value)) VALUE = SUM.value;
      }
    } else {
      for (I = 1; I <= N; I++) {
        WORK[I] = ZERO;
      }
      for (J = 1; J <= N; J++) {
        SUM.value = WORK[J] + A[J][J].abs();
        for (I = J + 1; I <= N; I++) {
          ABSA = A[I][J].abs();
          SUM.value += ABSA;
          WORK[I] += ABSA;
        }
        if (VALUE < SUM.value || disnan(SUM.value)) VALUE = SUM.value;
      }
    }
  } else if ((lsame(NORM, 'F')) || (lsame(NORM, 'E'))) {
    // Find normF(A).

    SCALE.value = ZERO;
    SUM.value = ONE;
    if (lsame(UPLO, 'U')) {
      for (J = 2; J <= N; J++) {
        dlassq(J - 1, A(1, J).asArray(), 1, SCALE, SUM);
      }
    } else {
      for (J = 1; J <= N - 1; J++) {
        dlassq(N - J, A(J + 1, J).asArray(), 1, SCALE, SUM);
      }
    }
    SUM.value = 2 * SUM.value;
    dlassq(N, A.asArray(), LDA + 1, SCALE, SUM);
    VALUE = SCALE.value * sqrt(SUM.value);
  }

  return VALUE;
}