Class model::AbstractPubo

Inheritance

model::GraphModel

model::AbstractPubo

Inherited Members

Model

state_only_memory_estimate

init

get_initial_configuration_state

calculate_cost

estimate_min_cost_diff

set_step_limit

state_memory_estimate

render

calculate_cost_difference

configure

~BaseModel

configure

BaseModel

edge

get_const_cost

node

rescale

node_count

estimate_max_cost_diff

get_term_count

get_scale_factor

is_empty

has_initial_configuration

is_rescaled

get_initial_configuration

get_sweep_size

get_benchmark_properties

edge_count

render

~Component

Component

get_status

param

get_class_name

Constructors

AbstractPubo()

Declaration

model::AbstractPubo<State_T, Cost_T>::AbstractPubo()

Methods

get_identifier()

Returns the identifier string of the model type (e.g., "ising").

Declaration

std::string model::AbstractPubo<State_T, Cost_T>::get_identifier() const override

get_version()

Returns the version of the input format this implementation expects.

Declaration

std::string model::AbstractPubo<State_T, Cost_T>::get_version() const override

match_version()

Checks the string argument against the expected version.

This can be overloaded to accept other version than the one returned by get_version().

Declaration

void model::AbstractPubo<State_T, Cost_T>::match_version(const std::string&version) override

~AbstractPubo()

Declaration

virtual model::AbstractPubo<State_T, Cost_T>::~AbstractPubo()

configure()

Populates model internals from the input json.

Declaration

void model::AbstractPubo<State_T, Cost_T>::configure(const utils::Json&json) override

configure()

Declaration

void model::AbstractPubo<State_T, Cost_T>::configure(typename Graph::Configuration_T&configuration)

get_random_state()

Return a valid random state for the model.

The various algorithms don't know how to initialize a valid state; this allows them to start with a random one. NOTE: The rng being passed should be used for randomness (as opposed to creating one for the model), as multiple threads can potentially use the same underlying model (albeit with separate rngs).

Declaration

State_T model::AbstractPubo<State_T, Cost_T>::get_random_state(utils::RandomGenerator&rng) const override

get_initial_configuration_state()

Declaration

State_T model::AbstractPubo<State_T, Cost_T>::get_initial_configuration_state() const override

get_random_transition()

Return a random transition starting at state.

The various algorithms don't know how to move throuhg markov space; this allows them to pick a random direction given a starting point.

By definition, the next markov state should only depend on the current one, so only the last one is passed to this function.
"random" does not necessitate equi-distributed here (you may choose a different distribution if your model dictates it). However, the the typical choise is to pick randomly from all possible moves at state with equal probability.

Declaration

Transition_T model::AbstractPubo<State_T, Cost_T>::get_random_transition(const State_T&, utils::RandomGenerator&rng) const override

apply_transition()

Apply a transition to a state.

This changes the configuration represented by *state. Depending on the optimization algorithm, transition can either be applied conditionally or alaways (e.g., population dynamics). Separating the functionality into the three interfaces random_transition, calculate_cost_difference, apply_transition leaves control over the strategy with the optimization method.

Declaration

virtual void model::AbstractPubo<State_T, Cost_T>::apply_transition(const Transition_T&transition, State_T&state) const override=0

render_state()

Render a state of this model.

Default implementation of how a state of this model is rendered (by' invoking the state's proper rendering mechanic). Overloading this method allows a model implementation to customize how a state is printed. This is relevant if the rendering needs to depend on model parameters in addition to the state.

Declaration

utils::Structure model::AbstractPubo<State_T, Cost_T>::render_state(const State_T&state) const override

state_memory_estimate()

Declaration

size_t model::AbstractPubo<State_T, Cost_T>::state_memory_estimate() const override

state_only_memory_estimate()

Declaration

size_t model::AbstractPubo<State_T, Cost_T>::state_only_memory_estimate() const override

estimate_min_cost_diff()

Declaration

double model::AbstractPubo<State_T, Cost_T>::estimate_min_cost_diff() const override

edges()

Return a vector of all the edges.

Declaration

const std::vector<Edge>&model::GraphModel<State_T, Transition_T, Cost_T>::edges() const

nodes()

Return a vector of all the nodes.

Declaration

const std::vector<Node>&model::GraphModel<State_T, Transition_T, Cost_T>::nodes() const