\(\newcommand{\W}[1]{ \; #1 \; }\) \(\newcommand{\R}[1]{ {\rm #1} }\) \(\newcommand{\B}[1]{ {\bf #1} }\) \(\newcommand{\D}[2]{ \frac{\partial #1}{\partial #2} }\) \(\newcommand{\DD}[3]{ \frac{\partial^2 #1}{\partial #2 \partial #3} }\) \(\newcommand{\Dpow}[2]{ \frac{\partial^{#1}}{\partial {#2}^{#1}} }\) \(\newcommand{\dpow}[2]{ \frac{ {\rm d}^{#1}}{{\rm d}\, {#2}^{#1}} }\)
rev_hes_sparsity¶
View page sourceReverse Mode Hessian Sparsity Patterns¶
Syntax¶
rev_hes_sparsity
(Purpose¶
We use \(F : \B{R}^n \rightarrow \B{R}^m\) to denote the AD Function corresponding to the operation sequence stored in f . Fix \(R \in \B{R}^{n \times \ell}\), \(s \in \B{R}^m\) and define the function
Given a Sparsity Pattern for \(R\)
and for the vector \(s\),
rev_hes_sparsity
computes a sparsity pattern for \(H(x)\).
x¶
Note that the sparsity pattern \(H(x)\) corresponds to the operation sequence stored in f and does not depend on the argument x .
BoolVector¶
The type BoolVector is a SimpleVector class with
elements of type
bool
.
SizeVector¶
The type SizeVector is a SimpleVector class with
elements of type
size_t
.
f¶
The object f has prototype
ADFun
< Base > f
R¶
The sparsity pattern for the matrix \(R\) is specified by pattern_in in the previous call
for_jac_sparsity
(select_range¶
The argument select_range has prototype
const
BoolVector & select_range
It has size \(m\) and specifies which components of the vector \(s\) are non-zero; i.e., select_range [ i ] is true if and only if \(s_i\) is possibly non-zero.
transpose¶
This argument has prototype
bool
transpose
See pattern_out below.
internal_bool¶
If this is true, calculations are done with sets represented by a vector
of boolean values. Otherwise, a vector of sets of integers is used.
This must be the same as in the previous call to
f . for_jac_sparsity
.
pattern_out¶
This argument has prototype
sparse_rc
< SizeVector >& pattern_out
This input value of pattern_out does not matter. If transpose it is false (true), upon return pattern_out is a sparsity pattern for \(H(x)\) (\(H(x)^\R{T}\)).
Sparsity for Entire Hessian¶
Suppose that \(R\) is the \(n \times n\) identity matrix. In this case, pattern_out is a sparsity pattern for \((s^\R{T} F)^{(2)} ( x )\).
Example¶
The file rev_hes_sparsity.cpp contains an example and test of this operation.