diff --git a/dev/index.html b/dev/index.html index 6ea71f6..783a81d 100644 --- a/dev/index.html +++ b/dev/index.html @@ -19,4 +19,4 @@ number = "4", pages = "1066--1095", DOI = "10.1137/19M1266265", -}

Docstrings

GCPDecompositions.GCPDecompositions — Module

Generalized CP Decomposition module. Provides approximate CP tensor decomposition with respect to general losses.

source
GCPDecompositions.CPD — Type
CPD

Tensor decomposition type for the canonical polyadic decompositions (CPD) of a tensor (i.e., a multi-dimensional array) A.

If F::CPD is the decomposition object, the weights λ and factor matrices U = (U[1],...,U[N]) can be obtained via F.λ and F.U, such that A = Σ_j λ[j] U[1][:,j] ∘ ⋯ ∘ U[N][:,j].

source
GCPDecompositions.gcp — Function
gcp(X::Array, r[, func, grad, lower]) -> CPD

Compute an approximate rank-r CP decomposition of the tensor X with respect to a general loss and return a CPD object.

Inputs

  • X : multi-dimensional tensor/array to approximate/decompose
  • r : number of components for the CPD
  • func : loss function, default = (x, m) -> (m - x)^2
  • grad : loss function derivative, default uses ForwardDiff.jl
  • lower : lower bound for factor matrix entries, default = -Inf
source
+}

Docstrings

GCPDecompositions.GCPDecompositions — Module

Generalized CP Decomposition module. Provides approximate CP tensor decomposition with respect to general losses.

source
GCPDecompositions.CPD — Type
CPD

Tensor decomposition type for the canonical polyadic decompositions (CPD) of a tensor (i.e., a multi-dimensional array) A.

If F::CPD is the decomposition object, the weights λ and factor matrices U = (U[1],...,U[N]) can be obtained via F.λ and F.U, such that A = Σ_j λ[j] U[1][:,j] ∘ ⋯ ∘ U[N][:,j].

source
GCPDecompositions.gcp — Function
gcp(X::Array, r[, func, grad, lower]) -> CPD

Compute an approximate rank-r CP decomposition of the tensor X with respect to a general loss and return a CPD object.

Inputs

  • X : multi-dimensional tensor/array to approximate/decompose
  • r : number of components for the CPD
  • func : loss function, default = (x, m) -> (m - x)^2
  • grad : loss function derivative, default uses ForwardDiff.jl
  • lower : lower bound for factor matrix entries, default = -Inf
source
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