Package slepc4py :: Module SLEPc :: Class BV

Class BV

BV

Nested Classes

[hide private]

BlockType
BV block-orthogonalization types

MatMultType
BV mat-mult types

OrthogBlockType
BV block-orthogonalization types

OrthogRefineType
BV orthogonalization refinement types

OrthogType
BV orthogonalization types

RefineType
BV orthogonalization refinement types

Type
BV type

Instance Methods

[hide private]

a new object with type S, a subtype of T

__new__(S, ...)

applyMatrix(self, Vec x, Vec y)
Multiplies a vector with the matrix associated to the bilinear form.

copy(self, BV result=None)
Copies a basis vector object into another one.

copyColumn(self, j, i)
Copies the values from one of the columns to another one.

copyVec(self, j, Vec v)
Copies one of the columns of a basis vectors object into a Vec.

create(self, comm=None)
Creates the BV object.

createFromMat(self, Mat A)
Creates a basis vectors object from a dense Mat object.

createMat(self)
Creates a new Mat object of dense type and copies the contents of the BV object.

createVec(self)
Creates a new Vec object with the same type and dimensions as the columns of the basis vectors object.

destroy(self)
Destroys the BV object.

dot(self, BV Y)
Computes the 'block-dot' product of two basis vectors objects.

dotColumn(self, j)
Computes multiple dot products of a column against all the column vectors of a BV.

dotVec(self, Vec v)
Computes multiple dot products of a vector against all the column vectors of a BV.

duplicate(self)
Duplicate the BV object with the same type and dimensions.

duplicateResize(self, m)
Creates a new BV object of the same type and dimensions as an existing one, but with possibly different number of columns.

getActiveColumns(self)
Returns the current active dimensions.

getColumn(self, j)
Returns a Vec object that contains the entries of the requested column of the basis vectors object.

getDefiniteTolerance(self)
Gets the tolerance to be used when checking a definite inner product.

getMat(self)
Returns a Mat object of dense type that shares the memory of the basis vectors object.

getMatMultMethod(self)
Gets the method used for the `matMult()` operation.

getMatrix(self)
Retrieves the matrix representation of the inner product.

getNumConstraints(self)
Gets the number of constraints.

getOptionsPrefix(self)
Gets the prefix used for searching for all BV options in the database.

getOrthogonalization(self)
Gets the orthogonalization settings from the BV object.

getRandomContext(self)
Gets the `PETSc.Random` object associated with the BV.

getSizes(self)
Returns the local and global sizes, and the number of columns.

getType(self)
Gets the BV type of this object.

insertConstraints(self, C)
Insert a set of vectors as constraints.

insertVec(self, j, Vec w)
Insert a vector into the specified column.

insertVecs(self, s, W, bool orth)
Insert a set of vectors into specified columns.

matMult(self, Mat A, BV Y=None)
Computes the matrix-vector product for each column, Y = A*V.

matMultColumn(self, Mat A, j)
Computes the matrix-vector product for a specified column, storing the result in the next column: v_{j+1}=A*v_j.

matMultHermitianTranspose(self, Mat A, BV Y=None)
Computes the matrix-vector product with the conjugate transpose of a matrix for each column, Y=A^H*V.

matMultHermitianTransposeColumn(self, Mat A, j)
Computes the conjugate-transpose matrix-vector product for a specified column, storing the result in the next column: v_{j+1}=A^H*v_j.

matMultTransposeColumn(self, Mat A, j)
Computes the transpose matrix-vector product for a specified column, storing the result in the next column: v_{j+1}=A^T*v_j.

matProject(self, Mat A, BV Y)
Computes the projection of a matrix onto a subspace.

mult(self, alpha, beta, BV X, Mat Q)
Computes Y = beta*Y + alpha*X*Q.

multColumn(self, alpha, beta, j, q)
Computes y = beta*y + alpha*X*q, where y is the j-th column.

multInPlace(self, Mat Q, s, e)
Update a set of vectors as V(:,s:e-1) = V*Q(:,s:e-1).

multVec(self, alpha, beta, Vec y, q)
Computes y = beta*y + alpha*X*q.

norm(self, norm_type=None)
Computes the matrix norm of the BV.

normColumn(self, int j, norm_type=None)
Computes the matrix norm of the BV.

orthogonalize(self, Mat R=None, **kargs)
Orthogonalize all columns (except leading ones), that is, compute the QR decomposition.

orthogonalizeColumn(self, j)
Orthogonalize one of the column vectors with respect to the previous ones.

orthogonalizeVec(self, Vec v)
Orthogonalize a vector with respect to a set of vectors.

orthonormalizeColumn(self, j, replace=False)
Orthonormalize one of the column vectors with respect to the previous ones.

resize(self, m, copy=True)
Change the number of columns.

restoreColumn(self, j, Vec v)
Restore a column obtained with `getColumn()`.

restoreMat(self, Mat A)
Restores the Mat obtained with `getMat()`.

scale(self, alpha)
Multiply the entries by a scalar value.

scaleColumn(self, j, alpha)
Scale column j by alpha

setActiveColumns(self, l, k)
Specify the columns that will be involved in operations.

setDefiniteTolerance(self, deftol)
Sets the tolerance to be used when checking a definite inner product.

setFromOptions(self)
Sets BV options from the options database.

setMatMultMethod(self, method)
Specifies the method used for the `matMult()` operation.

setMatrix(self, Mat mat, bool indef)
Sets the bilinear form to be used for inner products.

setNumConstraints(self, nc)
Sets the number of constraints.

setOptionsPrefix(self, prefix)
Sets the prefix used for searching for all BV options in the database.

setOrthogonalization(self, otype=None, refine=None, eta=None, block=None)
Specifies the method used for the orthogonalization of vectors (classical or modified Gram-Schmidt with or without refinement), and for the block-orthogonalization (simultaneous orthogonalization of a set of vectors).

setRandom(self)
Set the active columns of the BV to random numbers.

setRandomColumn(self, j)
Set one column of the BV to random numbers.

setRandomCond(self, condn)
Set the columns of a BV to random numbers, in a way that the generated matrix has a given condition number.

setRandomContext(self, Random rnd)
Sets the `PETSc.Random` object associated with the BV, to be used in operations that need random numbers.

setRandomNormal(self)
Set the active columns of the BV to random numbers (with normal distribution).

setRandomSign(self)
Set the entries of a BV to values 1 or -1 with equal probability.

setSizes(self, sizes, m)
Sets the local and global sizes, and the number of columns.

setSizesFromVec(self, Vec w, m)
Sets the local and global sizes, and the number of columns.

setType(self, bv_type)
Selects the type for the BV object.

view(self, Viewer viewer=None)
Prints the BV data structure.

Inherited from petsc4py.PETSc.Object: __copy__, __deepcopy__, __eq__, __ge__, __gt__, __le__, __lt__, __ne__, __nonzero__, compose, decRef, getAttr, getClassId, getClassName, getComm, getDict, getName, getRefCount, getTabLevel, incRef, incrementTabLevel, query, setAttr, setName, setTabLevel, stateIncrease, viewFromOptions

Inherited from object: __delattr__, __format__, __getattribute__, __hash__, __init__, __reduce__, __reduce_ex__, __repr__, __setattr__, __sizeof__, __str__, __subclasshook__

Properties

[hide private]

Inherited from petsc4py.PETSc.Object: classid, comm, fortran, handle, klass, name, prefix, refcount, type

Inherited from object: __class__

Method Details

[hide private]

new(S, ...)

Returns: a new object with type S, a subtype of T
Overrides: object.__new__

applyMatrix(self, Vec x, Vec y)

Multiplies a vector with the matrix associated to the bilinear
form.

Parameters
----------
x: Vec
   The input vector.
y: Vec
   The result vector.

Notes
-----
If the bilinear form has no associated matrix this function
copies the vector.

copy(self, BV result=None)

Copies a basis vector object into another one.

Parameters
----------
result: `BV`, optional
    The copy.

copyColumn(self, j, i)

Copies the values from one of the columns to another one.

Parameters
----------
j: int
    The number of the source column.
i: int
    The number of the destination column.

copyVec(self, j, Vec v)

Copies one of the columns of a basis vectors object into a Vec.

Parameters
----------
j: int
    The column number to be copied.
v: Vec
    A vector.

create(self, comm=None)

Creates the BV object.

Parameters
----------
comm: Comm, optional
      MPI communicator; if not provided, it defaults to all
      processes.

createFromMat(self, Mat A)

Creates a basis vectors object from a dense Mat object.

Parameters
----------
A: Mat
   A dense tall-skinny matrix.

createMat(self)

Creates a new Mat object of dense type and copies the contents of the
BV object.

Returns
-------
mat: the new matrix.

createVec(self)

Creates a new Vec object with the same type and dimensions as
the columns of the basis vectors object.

Returns
-------
v: Vec
   New vector.

destroy(self)

Destroys the BV object.

Overrides: petsc4py.PETSc.Object.destroy

dot(self, BV Y)

Computes the 'block-dot' product of two basis vectors objects.
    M = Y^H*X (m_ij = y_i^H x_j) or M = Y^H*B*X

Parameters
----------
Y: BV
    Left basis vectors, can be the same as self, giving M = X^H X.

Returns
-------
M: Mat
    The resulting matrix.

Notes
-----
This is the generalization of VecDot() for a collection of vectors, M =
Y^H*X. The result is a matrix M whose entry m_ij is equal to y_i^H x_j
(where y_i^H denotes the conjugate transpose of y_i).

X and Y can be the same object.

If a non-standard inner product has been specified with setMatrix(),
then the result is M = Y^H*B*X. In this case, both X and Y must have
the same associated matrix.

Only rows (resp. columns) of M starting from ly (resp. lx) are
computed, where ly (resp. lx) is the number of leading columns of Y
(resp. X).

dotColumn(self, j)

Computes multiple dot products of a column against all the column
vectors of a BV.

Parameters
----------
j: int
    The index of the column.

Returns
-------
m: array of scalars
    The computed values.

dotVec(self, Vec v)

Computes multiple dot products of a vector against all the column
vectors of a BV.

Parameters
----------
v: Vec
    A vector.

Returns
-------
m: array of scalars
    The computed values.

Notes
-----
This is analogue to VecMDot(), but using BV to represent a collection
of vectors. The result is m = X^H*y, so m_i is equal to x_j^H y. Note
that here X is transposed as opposed to BVDot().

If a non-standard inner product has been specified with BVSetMatrix(),
then the result is m = X^H*B*y.

duplicateResize(self, m)

Creates a new BV object of the same type and dimensions as
an existing one, but with possibly different number of columns.

Parameters
----------
m: int
    The number of columns.

getActiveColumns(self)

Returns the current active dimensions.

Returns
-------
l: int
    The leading number of columns.
k: int
    The active number of columns.

getColumn(self, j)

Returns a Vec object that contains the entries of the requested column
of the basis vectors object.

Parameters
----------
j: int
    The index of the requested column.

Returns
-------
v: Vec
    The vector containing the jth column.

Notes
-----
Modifying the returned Vec will change the BV entries as well.

getDefiniteTolerance(self)

Gets the tolerance to be used when checking a definite inner product.

Returns
-------
deftol: float
     The tolerance.

getMat(self)

Returns a Mat object of dense type that shares the memory
of the basis vectors object.

Returns
-------
A: Mat
   The matrix

Notes
-----
The returned matrix contains only the active columns. If the content
of the Mat is modified, these changes are also done in the BV object.
The user must call `restoreMat()` when no longer needed.

getMatMultMethod(self)

Gets the method used for the `matMult()` operation.

Returns
-------
method: `BV.MatMultType` enumerate
      The method for the `matMult()` operation.

getMatrix(self)

Retrieves the matrix representation of the inner product.

Returns
-------
mat: the matrix of the inner product

getNumConstraints(self)

Gets the number of constraints.

Returns
-------
nc: int
    The number of constraints.

getOptionsPrefix(self)

Gets the prefix used for searching for all BV options in the
database.

Returns
-------
prefix: string
        The prefix string set for this BV object.

Overrides: petsc4py.PETSc.Object.getOptionsPrefix

getOrthogonalization(self)

Gets the orthogonalization settings from the BV object.

Returns
-------
type: `BV.OrthogType` enumerate
      The type of orthogonalization technique.
refine: `BV.OrthogRefineType` enumerate
      The type of refinement.
eta:  float
      Parameter for selective refinement (used when the the
      refinement type `BV.OrthogRefineType.IFNEEDED`).
block: `BV.OrthogBlockType` enumerate
      The type of block orthogonalization .

getRandomContext(self)

Gets the `PETSc.Random` object associated with the BV.

Returns
-------
rnd: `PETSc.Random`
     The random number generator context.

getSizes(self)

Returns the local and global sizes, and the number of columns.

Returns
-------
sizes: two-tuple of int
        The local and global sizes ``(n, N)``.
m: int
        The number of columns.

getType(self)

Gets the BV type of this object.

Returns
-------
type: `BV.Type` enumerate
      The inner product type currently being used.

Overrides: petsc4py.PETSc.Object.getType

insertConstraints(self, C)

Insert a set of vectors as constraints.

Parameters
----------
C: Vec or sequence of Vec.
   Set of vectors to be inserted as constraints.

Returns
-------
nc: int
    Number of linearly independent vectors.

Notes
-----
The constraints are relevant only during orthogonalization. Constraint
vectors span a subspace that is deflated in every orthogonalization
operation, so they are intended for removing those directions from the
orthogonal basis computed in regular BV columns.

insertVec(self, j, Vec w)

Insert a vector into the specified column.

Parameters
----------
j: int
    The column to be overwritten.
w: Vec
    The vector to be copied.

insertVecs(self, s, W, bool orth)

Insert a set of vectors into specified columns.

Parameters
----------
s: int
    The first column to be overwritten.
W: Vec or sequence of Vec.
    Set of vectors to be copied.
orth:
    Flag indicating if the vectors must be orthogonalized.

Returns
-------
m: int
    Number of linearly independent vectors.

Notes
-----
Copies the contents of vectors W into self(:,s:s+n), where n is the
length of W. If orthogonalization flag is set then the vectors are
copied one by one then orthogonalized against the previous one.  If any
are linearly dependent then it is discared and the value of m is
decreased.

matMult(self, Mat A, BV Y=None)

Computes the matrix-vector product for each column, Y = A*V.

Parameters
----------
A: Mat
    The matrix.

Returns
-------
Y: BV
    The result.

Notes
-----
Only active columns (excluding the leading ones) are processed.

It is possible to choose whether the computation is done column by column
or using dense matrices using the options database keys:

    -bv_matmult_vecs
    -bv_matmult_mat

The default is bv_matmult_mat.

matMultColumn(self, Mat A, j)

Computes the matrix-vector product for a specified column, storing
the result in the next column: v_{j+1}=A*v_j.

Parameters
----------
A: Mat
    The matrix.
j: int
    Index of column.

matMultHermitianTranspose(self, Mat A, BV Y=None)

Computes the matrix-vector product with the conjugate transpose of a
matrix for each column, Y=A^H*V.

Parameters
----------
A: Mat
    The matrix.

Returns
-------
Y: BV
    The result.

Notes
-----
Only active columns (excluding the leading ones) are processed.

As opoosed to matMult(), this operation is always done by column by
column, with a sequence of calls to MatMultHermitianTranspose().

matMultHermitianTransposeColumn(self, Mat A, j)

Computes the conjugate-transpose matrix-vector product for a specified column,
storing the result in the next column: v_{j+1}=A^H*v_j.

Parameters
----------
A: Mat
    The matrix.
j: int
    Index of column.

matMultTransposeColumn(self, Mat A, j)

Computes the transpose matrix-vector product for a specified column,
storing the result in the next column: v_{j+1}=A^T*v_j.

Parameters
----------
A: Mat
    The matrix.
j: int
    Index of column.

matProject(self, Mat A, BV Y)

Computes the projection of a matrix onto a subspace.

M = Y^H A X

Parameters
----------
A: Mat or None
    Matrix to be projected.
Y: BV
    Left basis vectors, can be the same as self, giving M = X^H A X.

Returns
-------
M: Mat
    Projection of the matrix A onto the subspace.

mult(self, alpha, beta, BV X, Mat Q)

Computes Y = beta*Y + alpha*X*Q.

Parameters
----------
alpha: scalar
    Coefficient that multiplies X.
beta: scalar
    Coefficient that multiplies Y.
X: BV
    Input basis vectors.
Q: Mat
    Input matrix.

multColumn(self, alpha, beta, j, q)

Computes y = beta*y + alpha*X*q, where y is the j-th column.

Parameters
----------
alpha: scalar
    Coefficient that multiplies X.
beta: scalar
    Coefficient that multiplies y.
j: int
    The column index.
q: Array of scalar
    Input coefficients.

multInPlace(self, Mat Q, s, e)

Update a set of vectors as V(:,s:e-1) = V*Q(:,s:e-1).

Parameters
----------
Q: Mat
   A sequential dense matrix.
s: int
   First column to be overwritten.
e: int
   Last column to be overwritten.

multVec(self, alpha, beta, Vec y, q)

Computes y = beta*y + alpha*X*q.

Parameters
----------
alpha: scalar
    Coefficient that multiplies X.
beta: scalar
    Coefficient that multiplies y.
y: Vec
    Input/output vector.
q: Array of scalar
    Input coefficients.

norm(self, norm_type=None)

Computes the matrix norm of the BV.

Parameters
----------
norm_type: `PETSC.NormType` enumerate
    The norm type.

Returns
-------
norm: float

Notes
-----
All active columns (except the leading ones) are considered as a
matrix. The allowed norms are NORM_1, NORM_FROBENIUS, and
NORM_INFINITY.

This operation fails if a non-standard inner product has been specified
with BVSetMatrix().

normColumn(self, int j, norm_type=None)

Computes the matrix norm of the BV.

Parameters
----------
j: int
    Index of column.
norm_type: `PETSc.NormType` enumerate
    The norm type.

Returns
-------
norm: float

Notes
-----
The norm of V[j] is computed (NORM_1, NORM_2, or NORM_INFINITY).

If a non-standard inner product has been specified with BVSetMatrix(),
then the returned value is ``sqrt(V[j]'* B*V[j])``, where B is the inner
product matrix (argument 'type' is ignored).

orthogonalize(self, Mat R=None, **kargs)

Orthogonalize all columns (except leading ones),
that is, compute the QR decomposition.

Parameters
----------
R: Mat, optional
    A sequential dense matrix.

Notes
-----
The output satisfies ``V0 = V*R`` (where V0 represent the input V) and ``V'*V = I``.

orthogonalizeColumn(self, j)

Orthogonalize one of the column vectors with respect to the previous ones.

Parameters
----------
j: int
   Index of the column to be orthogonalized.

Returns
-------
norm: float
    The norm of the resulting vector.
lindep: bool
    Flag indicating that refinement did not improve the
    quality of orthogonalization.

Notes
-----
This function applies an orthogonal projector to project
vector ``V[j]`` onto the orthogonal complement of the span of the
columns ``V[0..j-1]``, where ``V[.]`` are the vectors of the BV.
The columns ``V[0..j-1]`` are assumed to be mutually orthonormal.

This routine does not normalize the resulting vector.

orthogonalizeVec(self, Vec v)

Orthogonalize a vector with respect to a set of vectors.

Parameters
----------
v:  Vec
    Vector to be orthogonalized, modified on return.

Returns
-------
norm: float
    The norm of the resulting vector.
lindep: bool
    Flag indicating that refinement did not improve the
    quality of orthogonalization.

Notes
-----
This function applies an orthogonal projector to project
vector ``v`` onto the orthogonal complement of the span of the
columns of the BV.

This routine does not normalize the resulting vector.

orthonormalizeColumn(self, j, replace=False)

Orthonormalize one of the column vectors with respect to the previous
ones.  This is equivalent to a call to `orthogonalizeColumn()`
followed by a call to `scaleColumn()` with the reciprocal of the norm.

Parameters
----------
j: int
   Index of the column to be orthonormalized.
replace: bool, optional
   Whether it is allowed to set the vector randomly.

Returns
-------
norm: float
    The norm of the resulting vector.
lindep: bool
    Flag indicating that refinement did not improve the
    quality of orthogonalization.

resize(self, m, copy=True)

Change the number of columns.

Parameters
----------
m: int
   The new number of columns.
copy: bool
   A flag indicating whether current values should be kept.

Notes
-----
Internal storage is reallocated. If copy is True, then the contents are
copied to the leading part of the new space.

restoreColumn(self, j, Vec v)

Restore a column obtained with `getColumn()`.

Parameters
----------
j: int
    The index of the requested column.
v: Vec
    The vector obtained with `getColumn()`.

Notes
-----
The arguments must match the corresponding call to `getColumn()`.

restoreMat(self, Mat A)

Restores the Mat obtained with `getMat()`.

Parameters
----------
A: Mat
   The matrix obtained with `getMat()`.

Notes
-----
A call to this function must match a previous call of `getMat()`.
The effect is that the contents of the Mat are copied back to the
BV internal data structures.

scale(self, alpha)

Multiply the entries by a scalar value.

Parameters
----------
alpha: float
    scaling factor.

Notes
-----
All active columns (except the leading ones) are scaled.

scaleColumn(self, j, alpha)

Scale column j by alpha

Parameters
----------
j: int
    column number to be scaled.
alpha: float
    scaling factor.

setActiveColumns(self, l, k)

Specify the columns that will be involved in operations.

Parameters
----------
l: int
    The leading number of columns.
k: int
    The active number of columns.

setDefiniteTolerance(self, deftol)

Sets the tolerance to be used when checking a definite inner product.

Parameters
----------
deftol: float
     The tolerance.

setFromOptions(self)

Sets BV options from the options database.

Notes
-----
To see all options, run your program with the ``-help``
option.

Overrides: petsc4py.PETSc.Object.setFromOptions

setMatMultMethod(self, method)

Specifies the method used for the `matMult()` operation.

Parameters
----------
method: `BV.MatMultType` enumerate
      The method for the `matMult()` operation.

setMatrix(self, Mat mat, bool indef)

Sets the bilinear form to be used for inner products.

Parameters
----------
mat:  Mat or None
      The matrix of the inner product.
indef: bool, optional
       Whether the matrix is indefinite

setNumConstraints(self, nc)

Sets the number of constraints.

Parameters
----------
nc: int
    The number of constraints.

setOptionsPrefix(self, prefix)

Sets the prefix used for searching for all BV options in the
database.

Parameters
----------
prefix: string
        The prefix string to prepend to all BV option
        requests.

Notes
-----
A hyphen (``-``) must NOT be given at the beginning of the
prefix name.  The first character of all runtime options is
AUTOMATICALLY the hyphen.

Overrides: petsc4py.PETSc.Object.setOptionsPrefix

setOrthogonalization(self, otype=None, refine=None, eta=None, block=None)

Specifies the method used for the orthogonalization of vectors
(classical or modified Gram-Schmidt with or without refinement),
and for the block-orthogonalization (simultaneous orthogonalization
of a set of vectors).

Parameters
----------
otype: `BV.OrthogType` enumerate, optional
      The type of orthogonalization technique.
refine: `BV.OrthogRefineType` enumerate, optional
      The type of refinement.
eta:  float, optional
      Parameter for selective refinement.
block: `BV.OrthogBlockType` enumerate, optional
      The type of block orthogonalization.

Notes
-----
The default settings work well for most problems.

The parameter `eta` should be a real value between ``0`` and
``1`` (or `DEFAULT`).  The value of `eta` is used only when
the refinement type is `BV.OrthogRefineType.IFNEEDED`.

When using several processors, `BV.OrthogType.MGS` is likely to
result in bad scalability.

If the method set for block orthogonalization is GS, then the
computation is done column by column with the vector orthogonalization.

setRandom(self)

Set the active columns of the BV to random numbers.

Notes
-----
All active columns (except the leading ones) are modified.

setRandomColumn(self, j)

Set one column of the BV to random numbers.

Parameters
----------
j: int
   Column number to be set.

setRandomCond(self, condn)

Set the columns of a BV to random numbers, in a way that the generated
matrix has a given condition number.

Parameters
----------
condn: float
       Condition number.

setRandomContext(self, Random rnd)

Sets the `PETSc.Random` object associated with the BV, to be used
in operations that need random numbers.

Parameters
----------
rnd: `PETSc.Random`
     The random number generator context.

setRandomNormal(self)

Set the active columns of the BV to random numbers (with normal
distribution).

Notes
-----
All active columns (except the leading ones) are modified.

setRandomSign(self)

Set the entries of a BV to values 1 or -1 with equal probability.

Notes
-----
All active columns (except the leading ones) are modified.

setSizes(self, sizes, m)

Sets the local and global sizes, and the number of columns.

Parameters
----------
sizes: int or two-tuple of int
      The global size ``N`` or a two-tuple ``(n, N)``
      with the local and global sizes.
m: int
      The number of columns.

Notes
-----
Either ``n`` or ``N`` (but not both) can be ``PETSc.DECIDE``
or ``None`` to have it automatically set.

setSizesFromVec(self, Vec w, m)

Sets the local and global sizes, and the number of columns. Local and
global sizes are specified indirectly by passing a template vector.

Parameters
----------
w: Vec
    The template vector.
m: int
    The number of columns.

setType(self, bv_type)

Selects the type for the BV object.

Parameters
----------
bv_type: `BV.Type` enumerate
          The inner product type to be used.

view(self, Viewer viewer=None)

Prints the BV data structure.

Parameters
----------
viewer: Viewer, optional
        Visualization context; if not provided, the standard
        output is used.

Overrides: petsc4py.PETSc.Object.view