#include <alpaqa/problem/type-erased-problem.hpp>
Detailed Description
class alpaqa::TypeErasedProblem< Conf, Allocator >
The main polymorphic minimization problem interface.
This class wraps the actual problem implementation class, filling in the missing member functions with sensible defaults, and providing a uniform interface that is used by the solvers.
The problem implementations do not inherit from an abstract base class. Instead, structural typing is used. The ProblemVTable constructor uses reflection to discover which member functions are provided by the problem implementation. See Problem formulations for more information, and C++/CustomCppProblem/main.cpp for an example.
Definition at line 219 of file type-erased-problem.hpp.
Inheritance diagram for TypeErasedProblem< Conf, Allocator >: Collaboration diagram for TypeErasedProblem< Conf, Allocator >:Problem dimensions | |
| length_t | get_n () const |
| [Required] Number of decision variables. | |
| length_t | get_m () const |
| [Required] Number of constraints. | |
Required cost and constraint functions | |
| real_t | eval_f (crvec x) const |
| [Required] Function that evaluates the cost, \( f(x) \) | |
| void | eval_grad_f (crvec x, rvec grad_fx) const |
| [Required] Function that evaluates the gradient of the cost, \( \nabla f(x) \) | |
| void | eval_g (crvec x, rvec gx) const |
| [Required] Function that evaluates the constraints, \( g(x) \) | |
| void | eval_grad_g_prod (crvec x, crvec y, rvec grad_gxy) const |
| [Required] Function that evaluates the gradient of the constraints times a vector, \( \nabla g(x)\,y = \tp{\jac_g(x)}y \) | |
Projections onto constraint sets and proximal mappings | |
| void | eval_proj_diff_g (crvec z, rvec e) const |
| [Required] Function that evaluates the difference between the given point \( z \) and its projection onto the constraint set \( D \). | |
| void | eval_proj_multipliers (rvec y, real_t M) const |
| [Required] Function that projects the Lagrange multipliers for ALM. | |
| real_t | eval_prox_grad_step (real_t γ, crvec x, crvec grad_ψ, rvec x̂, rvec p) const |
| [Required] Function that computes a proximal gradient step. | |
| index_t | eval_inactive_indices_res_lna (real_t γ, crvec x, crvec grad_ψ, rindexvec J) const |
| [Optional] Function that computes the inactive indices \( \mathcal J(x) \) for the evaluation of the linear Newton approximation of the residual, as in [2]. | |
Constraint sets | |
| const Box & | get_box_C () const |
| [Optional] Get the rectangular constraint set of the decision variables, \( x \in C \). | |
| const Box & | get_box_D () const |
| [Optional] Get the rectangular constraint set of the general constraint function, \( g(x) \in D \). | |
Functions for second-order solvers | |
| void | eval_jac_g (crvec x, rvec J_values) const |
| [Optional] Function that evaluates the nonzero values of the Jacobian matrix of the constraints, \( \jac_g(x) \) | |
| Sparsity | get_jac_g_sparsity () const |
| [Optional] Function that returns (a view of) the sparsity pattern of the Jacobian of the constraints. | |
| void | eval_grad_gi (crvec x, index_t i, rvec grad_gi) const |
| [Optional] Function that evaluates the gradient of one specific constraint, \( \nabla g_i(x) \) | |
| void | eval_hess_L_prod (crvec x, crvec y, real_t scale, crvec v, rvec Hv) const |
| [Optional] Function that evaluates the Hessian of the Lagrangian multiplied by a vector, \( \nabla_{xx}^2L(x, y)\,v \) | |
| void | eval_hess_L (crvec x, crvec y, real_t scale, rvec H_values) const |
| [Optional] Function that evaluates the nonzero values of the Hessian of the Lagrangian, \( \nabla_{xx}^2L(x, y) \) | |
| Sparsity | get_hess_L_sparsity () const |
| [Optional] Function that returns (a view of) the sparsity pattern of the Hessian of the Lagrangian. | |
| void | eval_hess_ψ_prod (crvec x, crvec y, crvec Σ, real_t scale, crvec v, rvec Hv) const |
| [Optional] Function that evaluates the Hessian of the augmented Lagrangian multiplied by a vector, \( \nabla_{xx}^2L_\Sigma(x, y)\,v \) | |
| void | eval_hess_ψ (crvec x, crvec y, crvec Σ, real_t scale, rvec H_values) const |
| [Optional] Function that evaluates the nonzero values of the Hessian of the augmented Lagrangian, \( \nabla_{xx}^2L_\Sigma(x, y) \) | |
| Sparsity | get_hess_ψ_sparsity () const |
| [Optional] Function that returns (a view of) the sparsity pattern of the Hessian of the augmented Lagrangian. | |
Combined evaluations | |
| real_t | eval_f_grad_f (crvec x, rvec grad_fx) const |
| [Optional] Evaluate both \( f(x) \) and its gradient, \( \nabla f(x) \). | |
| real_t | eval_f_g (crvec x, rvec g) const |
| [Optional] Evaluate both \( f(x) \) and \( g(x) \). | |
| void | eval_grad_f_grad_g_prod (crvec x, crvec y, rvec grad_f, rvec grad_gxy) const |
| [Optional] Evaluate both \( \nabla f(x) \) and \( \nabla g(x)\,y \). | |
| void | eval_grad_L (crvec x, crvec y, rvec grad_L, rvec work_n) const |
| [Optional] Evaluate the gradient of the Lagrangian \( \nabla_x L(x, y) = \nabla f(x) + \nabla g(x)\,y \) | |
Augmented Lagrangian | |
| real_t | eval_ψ (crvec x, crvec y, crvec Σ, rvec ŷ) const |
| [Optional] Calculate both ψ(x) and the vector ŷ that can later be used to compute ∇ψ. | |
| void | eval_grad_ψ (crvec x, crvec y, crvec Σ, rvec grad_ψ, rvec work_n, rvec work_m) const |
| [Optional] Calculate the gradient ∇ψ(x). | |
| real_t | eval_ψ_grad_ψ (crvec x, crvec y, crvec Σ, rvec grad_ψ, rvec work_n, rvec work_m) const |
| [Optional] Calculate both ψ(x) and its gradient ∇ψ(x). | |
Checks | |
| void | check () const |
| [Optional] Check that the problem formulation is well-defined, the dimensions match, etc. | |
Querying specialized implementations | |
| bool | provides_eval_inactive_indices_res_lna () const |
| Returns true if the problem provides an implementation of eval_inactive_indices_res_lna. | |
| bool | provides_eval_jac_g () const |
| Returns true if the problem provides an implementation of eval_jac_g. | |
| bool | provides_get_jac_g_sparsity () const |
| Returns true if the problem provides an implementation of get_jac_g_sparsity. | |
| bool | provides_eval_grad_gi () const |
| Returns true if the problem provides an implementation of eval_grad_gi. | |
| bool | provides_eval_hess_L_prod () const |
| Returns true if the problem provides an implementation of eval_hess_L_prod. | |
| bool | provides_eval_hess_L () const |
| Returns true if the problem provides an implementation of eval_hess_L. | |
| bool | provides_get_hess_L_sparsity () const |
| Returns true if the problem provides an implementation of get_hess_L_sparsity. | |
| bool | provides_eval_hess_ψ_prod () const |
| Returns true if the problem provides an implementation of eval_hess_ψ_prod. | |
| bool | provides_eval_hess_ψ () const |
| Returns true if the problem provides an implementation of eval_hess_ψ. | |
| bool | provides_get_hess_ψ_sparsity () const |
| Returns true if the problem provides an implementation of get_hess_ψ_sparsity. | |
| bool | provides_eval_f_grad_f () const |
| Returns true if the problem provides a specialized implementation of eval_f_grad_f, false if it uses the default implementation. | |
| bool | provides_eval_f_g () const |
| Returns true if the problem provides a specialized implementation of eval_f_g, false if it uses the default implementation. | |
| bool | provides_eval_grad_f_grad_g_prod () const |
| Returns true if the problem provides a specialized implementation of eval_grad_f_grad_g_prod, false if it uses the default implementation. | |
| bool | provides_eval_grad_L () const |
| Returns true if the problem provides a specialized implementation of eval_grad_L, false if it uses the default implementation. | |
| bool | provides_eval_ψ () const |
| Returns true if the problem provides a specialized implementation of eval_ψ, false if it uses the default implementation. | |
| bool | provides_eval_grad_ψ () const |
| Returns true if the problem provides a specialized implementation of eval_grad_ψ, false if it uses the default implementation. | |
| bool | provides_eval_ψ_grad_ψ () const |
| Returns true if the problem provides a specialized implementation of eval_ψ_grad_ψ, false if it uses the default implementation. | |
| bool | provides_get_box_C () const |
| Returns true if the problem provides an implementation of get_box_C. | |
| bool | provides_get_box_D () const |
| Returns true if the problem provides an implementation of get_box_D. | |
| bool | provides_check () const |
| Returns true if the problem provides an implementation of check. | |
Helpers | |
| real_t | calc_ŷ_dᵀŷ (rvec g_ŷ, crvec y, crvec Σ) const |
| Given g(x), compute the intermediate results ŷ and dᵀŷ that can later be used to compute ψ(x) and ∇ψ(x). | |
Public Types | |
| using | Sparsity = alpaqa::Sparsity< config_t > |
| using | Box = alpaqa::Box< config_t > |
| using | VTable = ProblemVTable< config_t > |
| using | allocator_type = Allocator |
| using | TypeErased = util::TypeErased< VTable, allocator_type > |
Public Member Functions | |
| TypeErased () noexcept(noexcept(allocator_type()))=default | |
| Default constructor. | |
| template<class Alloc > | |
| TypeErased (std::allocator_arg_t, const Alloc &alloc) | |
| Default constructor (allocator aware). | |
| TypeErased (const TypeErased &other) | |
| Copy constructor. | |
| TypeErased (const TypeErased &other, allocator_type alloc) | |
| Copy constructor (allocator aware). | |
| TypeErased (TypeErased &&other) noexcept | |
| Move constructor. | |
| TypeErased (TypeErased &&other, const allocator_type &alloc) noexcept | |
| Move constructor (allocator aware). | |
| template<class T , class Alloc > | |
| TypeErased (std::allocator_arg_t, const Alloc &alloc, T &&d) | |
| Main constructor that type-erases the given argument. | |
| template<class T , class Alloc , class... Args> | |
| TypeErased (std::allocator_arg_t, const Alloc &alloc, te_in_place_t< T >, Args &&...args) | |
| Main constructor that type-erases the object constructed from the given argument. | |
| template<class T > requires no_child_of_ours<T> | |
| TypeErased (T &&d) | |
| template<class T , class... Args> | |
| TypeErased (te_in_place_t< T >, Args &&...args) | |
| Main constructor that type-erases the object constructed from the given argument. | |
| operator bool () const noexcept | |
| Check if this wrapper wraps an object. | |
| allocator_type | get_allocator () const noexcept |
| Get a copy of the allocator. | |
| const std::type_info & | type () const noexcept |
| Query the contained type. | |
| template<class T > | |
| T & | as () & |
| Convert the type-erased object to the given type. | |
| template<class T > | |
| const T & | as () const & |
| Convert the type-erased object to the given type. | |
| template<class T > | |
| const T && | as () && |
| Convert the type-erased object to the given type. | |
Static Public Member Functions | |
| template<class T , class... Args> | |
| static TypeErasedProblem | make (Args &&...args) |
| template<class Ret , class T , class Alloc , class... Args> requires std::is_base_of_v<TypeErased, Ret> | |
| static Ret | make (std::allocator_arg_t tag, const Alloc &alloc, Args &&...args) |
| Construct a type-erased wrapper of type Ret for an object of type T, initialized in-place with the given arguments. | |
Static Public Attributes | |
| static constexpr size_t | small_buffer_size = SmallBufferSize |
Protected Member Functions | |
| template<class Ret , class... FArgs, class... Args> | |
| decltype(auto) | call (Ret(*f)(const void *, FArgs...), Args &&...args) const |
Call the vtable function f with the given arguments args, implicitly passing the self pointer and vtable reference if necessary. | |
| template<class Ret , class... FArgs, class... Args> | |
| decltype(auto) | call (Ret(*f)(void *, FArgs...), Args &&...args) |
Call the vtable function f with the given arguments args, implicitly passing the self pointer and vtable reference if necessary. | |
| template<class Ret > | |
| decltype(auto) | call (Ret(*f)(const void *)) const |
Call the vtable function f with the given arguments args, implicitly passing the self pointer and vtable reference if necessary. | |
| template<class Ret > | |
| decltype(auto) | call (Ret(*f)(void *)) |
Call the vtable function f with the given arguments args, implicitly passing the self pointer and vtable reference if necessary. | |
| template<class Ret > | |
| decltype(auto) | call (Ret(*f)(const void *, const VTable &)) const |
Call the vtable function f with the given arguments args, implicitly passing the self pointer and vtable reference if necessary. | |
| template<class Ret > | |
| decltype(auto) | call (Ret(*f)(void *, const VTable &)) |
Call the vtable function f with the given arguments args, implicitly passing the self pointer and vtable reference if necessary. | |
| template<class T , class... Args> | |
| void | construct_inplace (Args &&...args) |
| Ensure storage and construct the type-erased object of type T in-place. | |
| template<class Ret > | |
| decltype(auto) | call (Ret(*f)(const void *, const VTable &)) const |
Call the vtable function f with the given arguments args, implicitly passing the self pointer and vtable reference if necessary. | |
| template<class Ret > | |
| decltype(auto) | call (Ret(*f)(void *, const VTable &)) |
Call the vtable function f with the given arguments args, implicitly passing the self pointer and vtable reference if necessary. | |
Protected Attributes | |
| void * | self |
| Pointer to the stored object. | |
| VTable | vtable |
| size_t | size = invalid_size |
| Size required to store the object. | |
Static Protected Attributes | |
| static constexpr size_t | invalid_size |
Private Types | |
| using | allocator_traits = std::allocator_traits< allocator_type > |
| using | buffer_type = std::array< std::byte, small_buffer_size > |
Private Member Functions | |
| Deallocator | allocate (size_t size) |
| Ensure that storage is available, either by using the small buffer if it is large enough, or by calling the allocator. | |
| void | deallocate () |
| Deallocate the memory without invoking the destructor. | |
| void | cleanup () |
| Destroy the type-erased object (if not empty), and deallocate the memory if necessary. | |
| template<bool CopyAllocator> | |
| void | do_copy_assign (const TypeErased &other) |
Private Attributes | |
| buffer_type | small_buffer |
| allocator_type | allocator |
Static Private Attributes | |
| template<class T > | |
| static constexpr auto | no_child_of_ours |
True if T is not a child class of TypeErased. | |
Member Typedef Documentation
◆ Sparsity
| using Sparsity = alpaqa::Sparsity<config_t> |
Definition at line 222 of file type-erased-problem.hpp.
◆ Box
| using Box = alpaqa::Box<config_t> |
Definition at line 223 of file type-erased-problem.hpp.
◆ VTable
| using VTable = ProblemVTable<config_t> |
Definition at line 224 of file type-erased-problem.hpp.
◆ allocator_type
Definition at line 225 of file type-erased-problem.hpp.
◆ TypeErased
Definition at line 226 of file type-erased-problem.hpp.
◆ allocator_traits
|
privateinherited |
Definition at line 200 of file type-erasure.hpp.
◆ buffer_type
|
privateinherited |
Definition at line 201 of file type-erasure.hpp.
Member Function Documentation
◆ make() [1/2]
|
inlinestatic |
Definition at line 236 of file type-erased-problem.hpp.
◆ get_n()
[Required] Number of decision variables.
Definition at line 700 of file type-erased-problem.hpp.
Here is the caller graph for this function:◆ get_m()
[Required] Number of constraints.
Definition at line 704 of file type-erased-problem.hpp.
Here is the caller graph for this function:◆ eval_f()
[Required] Function that evaluates the cost, \( f(x) \)
- Parameters
-
[in] x Decision variable \( x \in \R^n \)
Definition at line 729 of file type-erased-problem.hpp.
Here is the caller graph for this function:◆ eval_grad_f()
[Required] Function that evaluates the gradient of the cost, \( \nabla f(x) \)
- Parameters
-
[in] x Decision variable \( x \in \R^n \) [out] grad_fx Gradient of cost function \( \nabla f(x) \in \R^n \)
Definition at line 733 of file type-erased-problem.hpp.
Here is the caller graph for this function:◆ eval_g()
[Required] Function that evaluates the constraints, \( g(x) \)
- Parameters
-
[in] x Decision variable \( x \in \R^n \) [out] gx Value of the constraints \( g(x) \in \R^m \)
Definition at line 737 of file type-erased-problem.hpp.
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[Required] Function that evaluates the gradient of the constraints times a vector, \( \nabla g(x)\,y = \tp{\jac_g(x)}y \)
- Parameters
-
[in] x Decision variable \( x \in \R^n \) [in] y Vector \( y \in \R^m \) to multiply the gradient by [out] grad_gxy Gradient of the constraints \( \nabla g(x)\,y \in \R^n \)
Definition at line 741 of file type-erased-problem.hpp.
◆ eval_proj_diff_g()
[Required] Function that evaluates the difference between the given point \( z \) and its projection onto the constraint set \( D \).
- Parameters
-
[in] z Slack variable, \( z \in \R^m \) [out] e The difference relative to its projection, \( e = z - \Pi_D(z) \in \R^m \)
- Note
zandecan refer to the same vector.
Definition at line 709 of file type-erased-problem.hpp.
Here is the caller graph for this function:◆ eval_proj_multipliers()
[Required] Function that projects the Lagrange multipliers for ALM.
- Parameters
-
[in,out] y Multipliers, \( y \leftarrow \Pi_Y(y) \in \R^m \) [in] M The radius/size of the set \( Y \). See ALMParams::max_multiplier.
Definition at line 713 of file type-erased-problem.hpp.
◆ eval_prox_grad_step()
[Required] Function that computes a proximal gradient step.
- Parameters
-
[in] γ Step size, \( \gamma \in \R_{>0} \) [in] x Decision variable \( x \in \R^n \) [in] grad_ψ Gradient of the subproblem cost, \( \nabla\psi(x) \in \R^n \) [out] x̂ Next proximal gradient iterate, \( \hat x = T_\gamma(x) = \prox_{\gamma h}(x - \gamma\nabla\psi(x)) \in \R^n \) [out] p The proximal gradient step, \( p = \hat x - x \in \R^n \)
- Returns
- The nonsmooth function evaluated at x̂, \( h(\hat x) \).
- Note
- The vector \( p \) is often used in stopping criteria, so its numerical accuracy is more important than that of \( \hat x \).
Definition at line 717 of file type-erased-problem.hpp.
Here is the caller graph for this function:◆ eval_inactive_indices_res_lna()
[Optional] Function that computes the inactive indices \( \mathcal J(x) \) for the evaluation of the linear Newton approximation of the residual, as in [2].
- Parameters
-
[in] γ Step size, \( \gamma \in \R_{>0} \) [in] x Decision variable \( x \in \R^n \) [in] grad_ψ Gradient of the subproblem cost, \( \nabla\psi(x) \in \R^n \) [out] J The indices of the components of \( x \) that are in the index set \( \mathcal J(x) \). In ascending order, at most n.
- Returns
- The number of inactive constraints, \( \# \mathcal J(x) \).
For example, in the case of box constraints, we have
\[ \mathcal J(x) \defeq \defset{i \in \N_{[0, n-1]}}{\underline x_i \lt x_i - \gamma\nabla_{\!x_i}\psi(x) \lt \overline x_i}. \]
Definition at line 722 of file type-erased-problem.hpp.
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[Optional] Get the rectangular constraint set of the decision variables, \( x \in C \).
Definition at line 821 of file type-erased-problem.hpp.
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[Optional] Get the rectangular constraint set of the general constraint function, \( g(x) \in D \).
Definition at line 825 of file type-erased-problem.hpp.
Here is the caller graph for this function:◆ eval_jac_g()
[Optional] Function that evaluates the nonzero values of the Jacobian matrix of the constraints, \( \jac_g(x) \)
- Parameters
-
[in] x Decision variable \( x \in \R^n \) [out] J_values Nonzero values of the Jacobian \( \jac_g(x) \in \R^{m\times n} \)
Required for second-order solvers only.
Definition at line 749 of file type-erased-problem.hpp.
Here is the caller graph for this function:◆ get_jac_g_sparsity()
[Optional] Function that returns (a view of) the sparsity pattern of the Jacobian of the constraints.
Required for second-order solvers only.
Definition at line 753 of file type-erased-problem.hpp.
Here is the caller graph for this function:◆ eval_grad_gi()
[Optional] Function that evaluates the gradient of one specific constraint, \( \nabla g_i(x) \)
- Parameters
-
[in] x Decision variable \( x \in \R^n \) [in] i Which constraint \( 0 \le i \lt m \) [out] grad_gi Gradient of the constraint \( \nabla g_i(x) \in \R^n \)
Required for second-order solvers only.
Definition at line 745 of file type-erased-problem.hpp.
◆ eval_hess_L_prod()
[Optional] Function that evaluates the Hessian of the Lagrangian multiplied by a vector, \( \nabla_{xx}^2L(x, y)\,v \)
- Parameters
-
[in] x Decision variable \( x \in \R^n \) [in] y Lagrange multipliers \( y \in \R^m \) [in] scale Scale factor for the cost function. [in] v Vector to multiply by \( v \in \R^n \) [out] Hv Hessian-vector product \( \nabla_{xx}^2 L(x, y)\,v \in \R^{n} \)
Required for second-order solvers only.
Definition at line 757 of file type-erased-problem.hpp.
◆ eval_hess_L()
[Optional] Function that evaluates the nonzero values of the Hessian of the Lagrangian, \( \nabla_{xx}^2L(x, y) \)
- Parameters
-
[in] x Decision variable \( x \in \R^n \) [in] y Lagrange multipliers \( y \in \R^m \) [in] scale Scale factor for the cost function. [out] H_values Nonzero values of the Hessian \( \nabla_{xx}^2 L(x, y) \in \R^{n\times n} \).
Required for second-order solvers only.
Definition at line 762 of file type-erased-problem.hpp.
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[Optional] Function that returns (a view of) the sparsity pattern of the Hessian of the Lagrangian.
Required for second-order solvers only.
Definition at line 767 of file type-erased-problem.hpp.
Here is the caller graph for this function:◆ eval_hess_ψ_prod()
[Optional] Function that evaluates the Hessian of the augmented Lagrangian multiplied by a vector, \( \nabla_{xx}^2L_\Sigma(x, y)\,v \)
- Parameters
-
[in] x Decision variable \( x \in \R^n \) [in] y Lagrange multipliers \( y \in \R^m \) [in] Σ Penalty weights \( \Sigma \) [in] scale Scale factor for the cost function. [in] v Vector to multiply by \( v \in \R^n \) [out] Hv Hessian-vector product \( \nabla_{xx}^2 L_\Sigma(x, y)\,v \in \R^{n} \)
Required for second-order solvers only.
Definition at line 771 of file type-erased-problem.hpp.
Here is the caller graph for this function:◆ eval_hess_ψ()
[Optional] Function that evaluates the nonzero values of the Hessian of the augmented Lagrangian, \( \nabla_{xx}^2L_\Sigma(x, y) \)
- Parameters
-
[in] x Decision variable \( x \in \R^n \) [in] y Lagrange multipliers \( y \in \R^m \) [in] Σ Penalty weights \( \Sigma \) [in] scale Scale factor for the cost function. [out] H_values Nonzero values of the Hessian \( \nabla_{xx}^2 L_\Sigma(x, y) \in \R^{n\times n} \)
Required for second-order solvers only.
Definition at line 776 of file type-erased-problem.hpp.
Here is the caller graph for this function:◆ get_hess_ψ_sparsity()
[Optional] Function that returns (a view of) the sparsity pattern of the Hessian of the augmented Lagrangian.
Required for second-order solvers only.
Definition at line 781 of file type-erased-problem.hpp.
Here is the caller graph for this function:◆ eval_f_grad_f()
[Optional] Evaluate both \( f(x) \) and its gradient, \( \nabla f(x) \).
- Default implementation:
- ProblemVTable::default_eval_f_grad_f
Definition at line 785 of file type-erased-problem.hpp.
Here is the caller graph for this function:◆ eval_f_g()
[Optional] Evaluate both \( f(x) \) and \( g(x) \).
- Default implementation:
- ProblemVTable::default_eval_f_g
Definition at line 789 of file type-erased-problem.hpp.
◆ eval_grad_f_grad_g_prod()
[Optional] Evaluate both \( \nabla f(x) \) and \( \nabla g(x)\,y \).
- Default implementation:
- ProblemVTable::default_eval_grad_f_grad_g_prod
Definition at line 793 of file type-erased-problem.hpp.
◆ eval_grad_L()
[Optional] Evaluate the gradient of the Lagrangian \( \nabla_x L(x, y) = \nabla f(x) + \nabla g(x)\,y \)
- Default implementation:
- ProblemVTable::default_eval_grad_L
Definition at line 798 of file type-erased-problem.hpp.
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[Optional] Calculate both ψ(x) and the vector ŷ that can later be used to compute ∇ψ.
\[ \psi(x) = f(x) + \tfrac{1}{2} \text{dist}_\Sigma^2\left(g(x) + \Sigma^{-1}y,\;D\right) \]
\[ \hat y = \Sigma\, \left(g(x) + \Sigma^{-1}y - \Pi_D\left(g(x) + \Sigma^{-1}y\right)\right) \]
- Default implementation:
- ProblemVTable::default_eval_ψ
- Parameters
-
[in] x Decision variable \( x \) [in] y Lagrange multipliers \( y \) [in] Σ Penalty weights \( \Sigma \) [out] ŷ \( \hat y \)
Definition at line 803 of file type-erased-problem.hpp.
Here is the caller graph for this function:◆ eval_grad_ψ()
[Optional] Calculate the gradient ∇ψ(x).
\[ \nabla \psi(x) = \nabla f(x) + \nabla g(x)\,\hat y(x) \]
- Default implementation:
- ProblemVTable::default_eval_grad_ψ
- Parameters
-
[in] x Decision variable \( x \) [in] y Lagrange multipliers \( y \) [in] Σ Penalty weights \( \Sigma \) [out] grad_ψ \( \nabla \psi(x) \) work_n Dimension \( n \) work_m Dimension \( m \)
Definition at line 807 of file type-erased-problem.hpp.
Here is the caller graph for this function:◆ eval_ψ_grad_ψ()
[Optional] Calculate both ψ(x) and its gradient ∇ψ(x).
\[ \psi(x) = f(x) + \tfrac{1}{2} \text{dist}_\Sigma^2\left(g(x) + \Sigma^{-1}y,\;D\right) \]
\[ \nabla \psi(x) = \nabla f(x) + \nabla g(x)\,\hat y(x) \]
- Default implementation:
- ProblemVTable::default_eval_ψ_grad_ψ
- Parameters
-
[in] x Decision variable \( x \) [in] y Lagrange multipliers \( y \) [in] Σ Penalty weights \( \Sigma \) [out] grad_ψ \( \nabla \psi(x) \) work_n Dimension \( n \) work_m Dimension \( m \)
Definition at line 812 of file type-erased-problem.hpp.
Here is the caller graph for this function:◆ check()
[Optional] Check that the problem formulation is well-defined, the dimensions match, etc.
Throws an exception if this is not the case.
Definition at line 829 of file type-erased-problem.hpp.
◆ provides_eval_inactive_indices_res_lna()
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inline |
Returns true if the problem provides an implementation of eval_inactive_indices_res_lna.
Definition at line 564 of file type-erased-problem.hpp.
Here is the caller graph for this function:◆ provides_eval_jac_g()
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inline |
Returns true if the problem provides an implementation of eval_jac_g.
Definition at line 569 of file type-erased-problem.hpp.
Here is the caller graph for this function:◆ provides_get_jac_g_sparsity()
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inline |
Returns true if the problem provides an implementation of get_jac_g_sparsity.
Definition at line 574 of file type-erased-problem.hpp.
◆ provides_eval_grad_gi()
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inline |
Returns true if the problem provides an implementation of eval_grad_gi.
Definition at line 579 of file type-erased-problem.hpp.
Here is the caller graph for this function:◆ provides_eval_hess_L_prod()
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inline |
Returns true if the problem provides an implementation of eval_hess_L_prod.
Definition at line 584 of file type-erased-problem.hpp.
Here is the caller graph for this function:◆ provides_eval_hess_L()
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inline |
Returns true if the problem provides an implementation of eval_hess_L.
Definition at line 589 of file type-erased-problem.hpp.
Here is the caller graph for this function:◆ provides_get_hess_L_sparsity()
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inline |
Returns true if the problem provides an implementation of get_hess_L_sparsity.
Definition at line 594 of file type-erased-problem.hpp.
◆ provides_eval_hess_ψ_prod()
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inline |
Returns true if the problem provides an implementation of eval_hess_ψ_prod.
Definition at line 599 of file type-erased-problem.hpp.
Here is the caller graph for this function:◆ provides_eval_hess_ψ()
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inline |
Returns true if the problem provides an implementation of eval_hess_ψ.
Definition at line 604 of file type-erased-problem.hpp.
Here is the caller graph for this function:◆ provides_get_hess_ψ_sparsity()
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inline |
Returns true if the problem provides an implementation of get_hess_ψ_sparsity.
Definition at line 609 of file type-erased-problem.hpp.
◆ provides_eval_f_grad_f()
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inline |
Returns true if the problem provides a specialized implementation of eval_f_grad_f, false if it uses the default implementation.
Definition at line 614 of file type-erased-problem.hpp.
Here is the caller graph for this function:◆ provides_eval_f_g()
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inline |
Returns true if the problem provides a specialized implementation of eval_f_g, false if it uses the default implementation.
Definition at line 619 of file type-erased-problem.hpp.
Here is the caller graph for this function:◆ provides_eval_grad_f_grad_g_prod()
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inline |
Returns true if the problem provides a specialized implementation of eval_grad_f_grad_g_prod, false if it uses the default implementation.
Definition at line 624 of file type-erased-problem.hpp.
Here is the caller graph for this function:◆ provides_eval_grad_L()
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inline |
Returns true if the problem provides a specialized implementation of eval_grad_L, false if it uses the default implementation.
Definition at line 629 of file type-erased-problem.hpp.
Here is the caller graph for this function:◆ provides_eval_ψ()
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inline |
Returns true if the problem provides a specialized implementation of eval_ψ, false if it uses the default implementation.
Definition at line 634 of file type-erased-problem.hpp.
Here is the caller graph for this function:◆ provides_eval_grad_ψ()
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inline |
Returns true if the problem provides a specialized implementation of eval_grad_ψ, false if it uses the default implementation.
Definition at line 637 of file type-erased-problem.hpp.
Here is the caller graph for this function:◆ provides_eval_ψ_grad_ψ()
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inline |
Returns true if the problem provides a specialized implementation of eval_ψ_grad_ψ, false if it uses the default implementation.
Definition at line 642 of file type-erased-problem.hpp.
Here is the caller graph for this function:◆ provides_get_box_C()
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inline |
Returns true if the problem provides an implementation of get_box_C.
Definition at line 647 of file type-erased-problem.hpp.
Here is the caller graph for this function:◆ provides_get_box_D()
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inline |
Returns true if the problem provides an implementation of get_box_D.
Definition at line 652 of file type-erased-problem.hpp.
Here is the caller graph for this function:◆ provides_check()
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inline |
Returns true if the problem provides an implementation of check.
Definition at line 656 of file type-erased-problem.hpp.
Here is the caller graph for this function:◆ calc_ŷ_dᵀŷ()
Given g(x), compute the intermediate results ŷ and dᵀŷ that can later be used to compute ψ(x) and ∇ψ(x).
Computes the result using the following algorithm:
\[ \begin{aligned} \zeta &= g(x) + \Sigma^{-1} y \\[] d &= \zeta - \Pi_D(\zeta) = \operatorname{eval\_proj\_diff\_g}(\zeta, \zeta) \\[] \hat y &= \Sigma d \\[] \end{aligned} \]
- See also
- page_math
- Parameters
-
[in,out] g_ŷ Input \( g(x) \), outputs \( \hat y \) [in] y Lagrange multipliers \( y \) [in] Σ Penalty weights \( \Sigma \)
- Returns
- The inner product \( d^\top \hat y \)
Definition at line 817 of file type-erased-problem.hpp.
◆ TypeErased() [1/10]
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defaultnoexcept |
Default constructor.
◆ TypeErased() [2/10]
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inline |
Default constructor (allocator aware).
Definition at line 225 of file type-erasure.hpp.
◆ TypeErased() [3/10]
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inline |
Copy constructor.
Definition at line 227 of file type-erasure.hpp.
◆ TypeErased() [4/10]
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inline |
Copy constructor (allocator aware).
Definition at line 233 of file type-erasure.hpp.
◆ TypeErased() [5/10]
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inlinenoexcept |
Move constructor.
Definition at line 249 of file type-erasure.hpp.
◆ TypeErased() [6/10]
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inlinenoexcept |
Move constructor (allocator aware).
Definition at line 267 of file type-erasure.hpp.
◆ TypeErased() [7/10]
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inlineexplicit |
Main constructor that type-erases the given argument.
Definition at line 352 of file type-erasure.hpp.
◆ TypeErased() [8/10]
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inlineexplicit |
Main constructor that type-erases the object constructed from the given argument.
Definition at line 359 of file type-erasure.hpp.
◆ TypeErased() [9/10]
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inlineexplicit |
Requirement prevents this constructor from taking precedence over the copy and move constructors.
Definition at line 369 of file type-erasure.hpp.
◆ TypeErased() [10/10]
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inlineexplicit |
Main constructor that type-erases the object constructed from the given argument.
Definition at line 375 of file type-erasure.hpp.
◆ call() [1/8]
Call the vtable function f with the given arguments args, implicitly passing the self pointer and vtable reference if necessary.
Definition at line 514 of file type-erasure.hpp.
◆ call() [2/8]
Call the vtable function f with the given arguments args, implicitly passing the self pointer and vtable reference if necessary.
Definition at line 526 of file type-erasure.hpp.
◆ call() [3/8]
Call the vtable function f with the given arguments args, implicitly passing the self pointer and vtable reference if necessary.
Definition at line 538 of file type-erasure.hpp.
◆ call() [4/8]
Call the vtable function f with the given arguments args, implicitly passing the self pointer and vtable reference if necessary.
Definition at line 545 of file type-erasure.hpp.
◆ call() [5/8]
Call the vtable function f with the given arguments args, implicitly passing the self pointer and vtable reference if necessary.
Definition at line 552 of file type-erasure.hpp.
◆ call() [6/8]
Call the vtable function f with the given arguments args, implicitly passing the self pointer and vtable reference if necessary.
Definition at line 560 of file type-erasure.hpp.
◆ make() [2/2]
requires std::is_base_of_v<TypeErased, Ret>
|
inlinestaticinherited |
Construct a type-erased wrapper of type Ret for an object of type T, initialized in-place with the given arguments.
Definition at line 383 of file type-erasure.hpp.
◆ operator bool()
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inlineexplicitnoexceptinherited |
Check if this wrapper wraps an object.
False for default-constructed objects.
Definition at line 400 of file type-erasure.hpp.
◆ get_allocator()
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inlinenoexceptinherited |
Get a copy of the allocator.
Definition at line 403 of file type-erasure.hpp.
◆ type()
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inlinenoexceptinherited |
Query the contained type.
Definition at line 406 of file type-erasure.hpp.
Here is the caller graph for this function:◆ as() [1/3]
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inlineinherited |
Convert the type-erased object to the given type.
The type is checked in debug builds only, use with caution.
Definition at line 413 of file type-erasure.hpp.
Here is the call graph for this function: Here is the caller graph for this function:◆ as() [2/3]
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inlineinherited |
Convert the type-erased object to the given type.
The type is checked in debug builds only, use with caution.
Definition at line 420 of file type-erasure.hpp.
Here is the call graph for this function:◆ as() [3/3]
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inlineinherited |
Convert the type-erased object to the given type.
The type is checked in debug builds only, use with caution.
Definition at line 427 of file type-erasure.hpp.
Here is the call graph for this function:◆ allocate()
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inlineprivateinherited |
Ensure that storage is available, either by using the small buffer if it is large enough, or by calling the allocator.
Returns a RAII wrapper that deallocates the storage unless released.
Definition at line 450 of file type-erasure.hpp.
Here is the caller graph for this function:◆ deallocate()
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inlineprivateinherited |
Deallocate the memory without invoking the destructor.
Definition at line 460 of file type-erasure.hpp.
Here is the caller graph for this function:◆ cleanup()
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inlineprivateinherited |
Destroy the type-erased object (if not empty), and deallocate the memory if necessary.
Definition at line 470 of file type-erasure.hpp.
Here is the call graph for this function: Here is the caller graph for this function:◆ do_copy_assign()
◆ construct_inplace()
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inlineprotectedinherited |
Ensure storage and construct the type-erased object of type T in-place.
Definition at line 498 of file type-erasure.hpp.
Here is the call graph for this function:◆ call() [7/8]
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inlineprotectedinherited |
Call the vtable function f with the given arguments args, implicitly passing the self pointer and vtable reference if necessary.
Definition at line 552 of file type-erasure.hpp.
◆ call() [8/8]
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inlineprotectedinherited |
Call the vtable function f with the given arguments args, implicitly passing the self pointer and vtable reference if necessary.
Definition at line 560 of file type-erasure.hpp.
Member Data Documentation
◆ self
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protected |
Pointer to the stored object.
Definition at line 215 of file type-erasure.hpp.
◆ vtable
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protected |
Definition at line 218 of file type-erasure.hpp.
◆ small_buffer_size
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staticconstexprinherited |
Definition at line 196 of file type-erasure.hpp.
◆ small_buffer
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privateinherited |
Definition at line 202 of file type-erasure.hpp.
◆ allocator
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privateinherited |
Definition at line 203 of file type-erasure.hpp.
◆ no_child_of_ours
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staticconstexprprivateinherited |
True if T is not a child class of TypeErased.
Definition at line 208 of file type-erasure.hpp.
◆ invalid_size
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staticconstexprprotectedinherited |
Definition at line 212 of file type-erasure.hpp.
◆ size
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protectedinherited |
Size required to store the object.
Definition at line 217 of file type-erasure.hpp.
The documentation for this class was generated from the following file:
- src/alpaqa/include/alpaqa/problem/type-erased-problem.hpp
