SelfAdjointEig

public final class SelfAdjointEig

Computes the eigen decomposition of a batch of self-adjoint matrices

(Note: Only real inputs are supported).

Computes the eigenvalues and eigenvectors of the innermost N-by-N matrices in tensor such that tensor[...,:,:] * v[..., :,i] = e[..., i] * v[...,:,i], for i=0...N-1.

Constants

String OP_NAME The name of this op, as known by TensorFlow core engine

Public Methods

static <T extends TType> SelfAdjointEig<T>
create(Scope scope, Operand<T> a, Boolean lower, Long maxIter, Float epsilon)
Factory method to create a class wrapping a new SelfAdjointEig operation.
Output<T>
v()
The column v[..., :, i] is the normalized eigenvector corresponding to the eigenvalue w[..., i].
Output<T>
w()
The eigenvalues in ascending order, each repeated according to its multiplicity.

Inherited Methods

org.tensorflow.op.RawOp
final boolean
equals(Object obj)
final int
hashCode()
Operation
op()
Return this unit of computation as a single Operation.
final String
toString()
boolean
equals(Object arg0)
final Class<?>
getClass()
int
hashCode()
final void
notify()
final void
notifyAll()
String
toString()
final void
wait(long arg0, int arg1)
final void
wait(long arg0)
final void
wait()
org.tensorflow.op.Op
abstract ExecutionEnvironment
env()
Return the execution environment this op was created in.
abstract Operation
op()
Return this unit of computation as a single Operation.

Constants

public static final String OP_NAME

The name of this op, as known by TensorFlow core engine

Constant Value: "XlaSelfAdjointEig"

Public Methods

public static SelfAdjointEig<T> create (Scope scope, Operand<T> a, Boolean lower, Long maxIter, Float epsilon)

Factory method to create a class wrapping a new SelfAdjointEig operation.

Parameters
scope current scope
a the input tensor.
lower a boolean specifies whether the calculation is done with the lower triangular part or the upper triangular part.
maxIter maximum number of sweep update, i.e., the whole lower triangular part or upper triangular part based on parameter lower. Heuristically, it has been argued that approximately logN sweeps are needed in practice (Ref: Golub & van Loan "Matrix Computation").
epsilon the tolerance ratio.
Returns
  • a new instance of SelfAdjointEig

public Output<T> v ()

The column v[..., :, i] is the normalized eigenvector corresponding to the eigenvalue w[..., i].

public Output<T> w ()

The eigenvalues in ascending order, each repeated according to its multiplicity.