HMatrix¶

class
HMatrix
(*args)¶ Hierarchical matrices.
Hierarchical matrices (or HMatrix) are a compressed representation of dense matrices. In many applications, matrix coefficients represent an interaction between two degrees of freedom; when these interactions are smooth, it is possible to approximate subblocks by a local lowrank approximation B =~ UV^T where B has dimension (m,n), U (m,k), and V (n,k). Of course, this is interesting only if k is much lower than m and n.
In order to obtain this compressed representation, several different steps must be performed:
Clustering: creation of rows and columns cluster trees Vertices where interactions are computed are reordered to improve locality. A binary space partition algorithm is used to recursively divide vertex set. Root cell contains all vertices. At each recursion step, a cell is divided into two new cells until it contains less than a given number of vertices. Space partition is performed orthogonally to original axis, by cutting its longest dimension.
The ‘median’ clustering algorithm divides a cell into two cells containing the same number of degrees of freedom.
The ‘geometric’ clustering algorithm divides a cell into two cells of the same geometric size
The ‘hybrid’ clustering algorithm is a mix. It first performs a ‘median’ bisection; if volumes of these new cells are very different, a ‘geometric’ clustering is performed instead.
Admissibility: creation of an empty HMatrix structure The first step created a full binary tree for rows and columns degrees of freedom. We will now create a hierarchical representation of our matrix by checking whether some blocks can be replaced by lowrank approximations. The whole matrix represents the interactions of all rows degrees of freedom against all columns degrees of freedom. It can not be approximated by a lowrank approximation, and thus it is replaced by 4 blocks obtained by considering interactions between rows and columns children nodes. This operation is performed recursively. At each step, we compute axis aligned bounding boxes rows_bbox and cols_bbox: if min(diameter(rows_bbox), diameter(cols_bbox)) <= eta*distance(rows_bbox, cols_bbox) then we consider that interaction between rows and columns degrees of freedom can have a local lowrank approximation, and recursion is stopped. Otherwise, we recurse until bottom cluster tree is reached. The whole matrix is thus represented by a 4tree where leaves will contain either lowrank approximation or full blocks. The eta parameter is called the admissibility factor, and it can be modified.
Assembly: coefficients computations The hierarchical structure of the matrix has been computed during step 2. To compute coefficients, we call the assemble method and provide a callable to compute interaction between two nodes. Full blocks are computed by calling this callable for the whole block. If compression method is ‘SVD’, lowrank approximation is computed by first computing the whole block, then finding its singular value decomposition, and rank is truncated so that error does not exceed assemblyEpsilon. This method is precise, but very costly. If compression method is a variant of ACA, only few rows and columns are computed. This is much more efficient, but error may be larger than expected on some problems.
Matrix computations Once an HMatrix is computed, usual linear algebra operations can be performed. Matrix can be factorized inplace, in order to solve systems. Or we can compute its product by a matrix or vector. But keep in mind that rows and columns are reordered internally, and thus results may differ sensibly from standard dense representation (for instance when computing a Cholesky or LU decomposition).
See also
 Attributes
thisown
The membership flag
Methods
assemble
(*args)Assemble matrix.
assembleReal
(callable, symmetry)Assemble matrix.
assembleTensor
(callable, outputDimension, …)Assemble matrix by block.
Compression ratio accessor.
copy
()Copy matrix.
dump
(name)Save matrix to a file.
factorize
(method)Factorize matrix.
Block ratio accessor.
gemm
(transA, transB, alpha, a, b, beta)Multiply matrix inplace self=alpha*op(A)*op(B)+beta*self.
gemv
(trans, alpha, x, beta, y)Multiply vector inplace y=alpha*op(A)*x+beta*y.
Accessor to the object’s name.
Diagonal values accessor.
getId
()Accessor to the object’s id.
getImplementation
(*args)Accessor to the underlying implementation.
getName
()Accessor to the object’s name.
Accessor to the number of columns.
Accessor to the number of rows.
norm
()Compute norm value.
scale
(alpha)Scale matrix inplace A=alpha*A.
setName
(name)Accessor to the object’s name.
solve
(*args)Solve linear system op(A)*x=b, after A has been factorized.
solveLower
(*args)Solve lower linear system op(L)*x=b, after A has been factorized.
Transpose matrix inplace.

__init__
(*args)¶ Initialize self. See help(type(self)) for accurate signature.

assemble
(*args)¶ Assemble matrix.
 Parameters
 f
HMatrixRealAssemblyFunction
orHMatrixTensorRealAssemblyFunction
Assembly function.
 symmetrystr
Symmetry flag, either N or L
 f

assembleReal
(callable, symmetry)¶ Assemble matrix.
 Parameters
 fassembly function
Callable that takes i,j int parameters and returns a float
 symmetrystr
Symmetry flag, either N or L

assembleTensor
(callable, outputDimension, symmetry)¶ Assemble matrix by block.
 Parameters
 fassembly function
Callable that takes i,j int parameters and returns a Matrix
 outputDimensionint
Block dimension
 symmetrystr
Symmetry flag, either N or L

compressionRatio
()¶ Compression ratio accessor.
 Returns
 ratio2tuple of int
Numbers of elements in the compressed and uncompressed forms.

copy
()¶ Copy matrix.
As factorization overwrites matrix contents, this method is useful to get a copy of assembled matrix before it is factorized.
 Returns
 matrix
HMatrix
Matrix copy.
 matrix

dump
(name)¶ Save matrix to a file.
 Parameters
 fileNamestr
File name to save to.

factorize
(method)¶ Factorize matrix.
 Parameters
 methodstr
Factorization method, either one of: LDLt, LLt or LU

fullrkRatio
()¶ Block ratio accessor.
 Returns
 ratio2tuple of int
Numbers of elements in full blocks and low rank blocks.

gemm
(transA, transB, alpha, a, b, beta)¶ Multiply matrix inplace self=alpha*op(A)*op(B)+beta*self.

gemv
(trans, alpha, x, beta, y)¶ Multiply vector inplace y=alpha*op(A)*x+beta*y.
 Parameters
 transstr
Whether to use A or A^t: either N or T.
 alphafloat
Coefficient
 xsequence of float
Vector to multiply.
 betafloat
Coefficient.
 y
Point
Vector multiplied inplace.

getClassName
()¶ Accessor to the object’s name.
 Returns
 class_namestr
The object class name (object.__class__.__name__).

getId
()¶ Accessor to the object’s id.
 Returns
 idint
Internal unique identifier.

getImplementation
(*args)¶ Accessor to the underlying implementation.
 Returns
 implImplementation
The implementation class.

getName
()¶ Accessor to the object’s name.
 Returns
 namestr
The name of the object.

getNbColumns
()¶ Accessor to the number of columns.
 Returns
 nbColumnsint
Number of columns.

getNbRows
()¶ Accessor to the number of rows.
 Returns
 nbRowsint
Number of rows.

norm
()¶ Compute norm value.
 Returns
 normfloat
Frobenius norm.

scale
(alpha)¶ Scale matrix inplace A=alpha*A.
 Parameters
 alphafloat
Coefficient.

setName
(name)¶ Accessor to the object’s name.
 Parameters
 namestr
The name of the object.

solve
(*args)¶ Solve linear system op(A)*x=b, after A has been factorized.

solveLower
(*args)¶ Solve lower linear system op(L)*x=b, after A has been factorized.

transpose
()¶ Transpose matrix inplace.