#include <deal.II/differentiation/sd/symengine_optimizer.h>

Public Member Functions
	BatchOptimizer ()

	BatchOptimizer (const enum OptimizerType &optimization_method, const enum OptimizationFlags &optimization_flags=OptimizationFlags::optimize_all)

	BatchOptimizer (const BatchOptimizer &other)

	BatchOptimizer (BatchOptimizer &&)=default

	~BatchOptimizer ()=default

void	copy_from (const BatchOptimizer &other)

template<typename Stream >
void	print (Stream &stream, const bool print_cse=false) const

template<class Archive >
void	save (Archive &archive, const unsigned int version) const

template<class Archive >
void	load (Archive &archive, const unsigned int version)

template<class Archive >
void	serialize (Archive &archive, const unsigned int version)

Independent variables
void	register_symbols (const types::substitution_map &substitution_map)

void	register_symbols (const SymEngine::map_basic_basic &substitution_map)

void	register_symbols (const types::symbol_vector &symbols)

void	register_symbols (const SymEngine::vec_basic &symbols)

types::symbol_vector	get_independent_symbols () const

std::size_t	n_independent_variables () const

Dependent variables
void	register_function (const Expression &function)

template<int rank, int dim>
void	register_function (const Tensor< rank, dim, Expression > &function_tensor)

template<int rank, int dim>
void	register_function (const SymmetricTensor< rank, dim, Expression > &function_tensor)

void	register_functions (const types::symbol_vector &functions)

void	register_functions (const SymEngine::vec_basic &functions)

template<typename T >
void	register_functions (const std::vector< T > &functions)

template<typename T , typename... Args>
void	register_functions (const T &functions, const Args &...other_functions)

const types::symbol_vector &	get_dependent_functions () const

std::size_t	n_dependent_variables () const

Optimization
void	set_optimization_method (const enum OptimizerType &optimization_method, const enum OptimizationFlags &optimization_flags=OptimizationFlags::optimize_all)

enum OptimizerType	optimization_method () const

enum OptimizationFlags	optimization_flags () const

bool	use_symbolic_CSE () const

void	optimize ()

bool	optimized () const

Symbol substitution
void	substitute (const types::substitution_map &substitution_map) const

void	substitute (const SymEngine::map_basic_basic &substitution_map) const

void	substitute (const types::symbol_vector &symbols, const std::vector< ReturnType > &values) const

void	substitute (const SymEngine::vec_basic &symbols, const std::vector< ReturnType > &values) const

bool	values_substituted () const

Evaluation / data extraction
using	map_dependent_expression_to_vector_entry_t = std::map< SD::Expression, std::size_t, SD::types::internal::ExpressionKeyLess >

enum OptimizerType	method

enum OptimizationFlags	flags

types::substitution_map	independent_variables_symbols

types::symbol_vector	dependent_variables_functions

std::vector< ReturnType >	dependent_variables_output

map_dependent_expression_to_vector_entry_t	map_dep_expr_vec_entry

std::unique_ptr< SymEngine::Visitor >	optimizer

bool	ready_for_value_extraction

bool	has_been_serialized

const std::vector< ReturnType > &	evaluate () const

ReturnType	evaluate (const Expression &func) const

std::vector< ReturnType >	evaluate (const std::vector< Expression > &funcs) const

template<int rank, int dim>
Tensor< rank, dim, ReturnType >	evaluate (const Tensor< rank, dim, Expression > &funcs) const

template<int rank, int dim>
SymmetricTensor< rank, dim, ReturnType >	evaluate (const SymmetricTensor< rank, dim, Expression > &funcs) const

ReturnType	extract (const Expression &func, const std::vector< ReturnType > &cached_evaluation) const

std::vector< ReturnType >	extract (const std::vector< Expression > &funcs, const std::vector< ReturnType > &cached_evaluation) const

template<int rank, int dim>
Tensor< rank, dim, ReturnType >	extract (const Tensor< rank, dim, Expression > &funcs, const std::vector< ReturnType > &cached_evaluation) const

template<int rank, int dim>
SymmetricTensor< rank, dim, ReturnType >	extract (const SymmetricTensor< rank, dim, Expression > &funcs, const std::vector< ReturnType > &cached_evaluation) const

bool	is_valid_nonunique_dependent_variable (const SD::Expression &function) const

bool	is_valid_nonunique_dependent_variable (const SymEngine::RCP< const SymEngine::Basic > &function) const

void	register_scalar_function (const SD::Expression &function)

void	register_vector_functions (const types::symbol_vector &functions)

void	create_optimizer (std::unique_ptr< SymEngine::Visitor > &optimizer)

void	substitute (const std::vector< ReturnType > &substitution_values) const

Detailed Description

template<typename ReturnType>
class Differentiation::SD::BatchOptimizer< ReturnType >

A class that facilitates the optimization of symbol expressions.

This expression will be optimized by this class; that is to say that the code path taken to substitute the set of (independent) symbols into a collection of (dependent) symbolic functions will be optimized using a chosen approach.

This snippet of pseudo-code describes the general usage of this class:

// Define some independent variables
const Expression x("x");
const Expression y("y");
...
 
// Compute some symbolic expressions that are dependent on the
// independent variables. These could be, for example, scalar
// expressions or tensors of expressions.
const auto f = calculate_f(x, y, ...);
const auto g = calculate_g(x, y, ...);
...
 
// Now create a optimizer to evaluate the dependent functions.
// The numerical result will be of type double, and a "lambda" optimizer,
// which employs common subexpression elimination, will be used.
using ReturnType = double;
BatchOptimizer<ReturnType> optimizer (OptimizerType::lambda,
                                      OptimizationFlags::optimize_cse);
 
// Register symbols that represent independent variables...
optimizer.register_symbols(x, y, ...);
// ... and symbolic expressions that represent dependent functions.
optimizer.register_functions(f, g, ...);
 
// Now we determine an equivalent code path that will evaluate
// all of the dependent functions at once, but with less computational
// cost than when evaluating the symbolic expression directly.
optimizer.optimize(); // Note: This is an expensive call.
 
// Next we pass the optimizer the numeric values that we wish the
// independent variables to represent.
const auto substitution_map
  = make_substitution_map({x, ...}, {y, ...}, ...);
// When making this next call, the call path used to (numerically)
// evaluate the dependent functions is quicker than dictionary
// substitution.
optimizer.substitute(substitution_map);
 
// Finally, we can get the numeric equivalent of the dependent functions
// from the optimizer.
const auto result_f = optimizer.evaluate(f);
const auto result_g = optimizer.evaluate(g);

Since the call to optimize() may be quite costly, there are a few "best practices" that can be adopted in order to mitigate this cost as much as possible:

Reuse a single instance of the class as much as possible. The most obvious way that this can be achieved would be to place an instance of this class in a centralized location where it can potentially be used by multiple calling functions and objects, if contextually possible.
Another form of reuse would entail generalizing the dependent functions/expressions to be evaluated by the optimizer as much as possible. For example, material coefficients need not necessarily be hard-coded, and one generalized statement of a constitutive law could then be broadly used in other material subdomains governed by the same class of constitutive law, but with different constitutive parameters. The same principle applies if using symbolic expressions to describe boundary conditions, systems of linear equations, etc.
When possible, consider using serialization to save and load the state of an optimizer that has already "optimized", i.e. it has been placed in a state where it is ready to evaluate expressions. With the exception of "lambda" optimization, all other forms of optimization permit checkpointing, meaning that the optimization could be done up front before executing the main body of code. It could also be used to duplicate an optimizer in an efficient manner, should multiple instances of the same optimizer be required.

Template Parameters

ReturnType The number type that is to be returned after value substitution and evaluation. Floating point and complex numbers are currently supported.

Warning: This class is not thread-safe.; The LLVM optimizer does not yet support complex numbers. If this incompatible combination of ReturnType and optimization method are selected, then an error will be thrown at run time.

Definition at line 1446 of file symengine_optimizer.h.

The documentation for this class was generated from the following files:

include/deal.II/differentiation/sd/symengine_optimizer.h
source/differentiation/sd/symengine_optimizer.cc

Public Member Functions

Evaluation / data extraction

Detailed Description