Struct smoothed_online_convex_optimization::problem::SimplifiedSmoothedConvexOptimization

pub struct SimplifiedSmoothedConvexOptimization<'a, T, C, D> {
    pub d: i32,
    pub t_end: i32,
    pub bounds: Vec<T>,
    pub switching_cost: Vec<f64>,
    pub hitting_cost: CostFn<'a, Config<T>, C, D>,
}

Simplified Smoothed Convex Optimization (SSCO).

• decision space is lower bounded by $\mathbf{0}$
• movement costs are a dimension-dependently scaled Manhattan distance

Fields

d: i32

Number of dimensions.

t_end: i32

Finite, positive time horizon.

bounds: Vec<T>

Vector of upper bounds of each dimension.

switching_cost: Vec<f64>

Vector of positive real constants resembling the switching cost of each dimension.

hitting_cost: CostFn<'a, Config<T>, C, D>

Non-negative convex cost functions.

Implementations

impl<'a, T, C, D> SimplifiedSmoothedConvexOptimization<'a, T, C, D> where
    T: Value<'a>,
    C: ModelOutputSuccess + 'a,
    D: ModelOutputFailure + 'a, 

pub fn into_sco(self) -> SmoothedConvexOptimization<'a, T, C, D>

Convert to an instance of Smoothed Convex Optimization. This assumes that time slots are added after this conversion.

Trait Implementations

impl<'a, T, C, D> BaseProblem for SimplifiedSmoothedConvexOptimization<'a, T, C, D> where
    T: Value<'a>,
    C: Clone,
    D: Clone, 

fn d(&self) -> i32

Number of dimensions.

fn t_end(&self) -> i32

Finite, positive time horizon.

fn set_t_end(&mut self, t_end: i32)

Updates the time horizon.

fn inc_t_end(&mut self)

Increases the time horizon by one time step.

impl<'a, T: Clone, C: Clone, D: Clone> Clone for SimplifiedSmoothedConvexOptimization<'a, T, C, D>

fn clone(&self) -> SimplifiedSmoothedConvexOptimization<'a, T, C, D>

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<'a, T, C, D> Debug for SimplifiedSmoothedConvexOptimization<'a, T, C, D> where
    T: Debug, 

fn fmt(&self, __f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<'a, C, D> DefaultGivenOnlineProblem<f64, SimplifiedSmoothedConvexOptimization<'a, f64, C, D>, C, D> for Memory where
    C: ModelOutputSuccess,
    D: ModelOutputFailure, 

fn default(
    o: &Online<FractionalSimplifiedSmoothedConvexOptimization<'_, C, D>>
) -> Self

impl<'a, T> Model<T, SimplifiedSmoothedConvexOptimization<'a, T, DataCenterModelOutputSuccess, DataCenterModelOutputFailure>, DataCenterOfflineInput, DataCenterOnlineInput, DataCenterModelOutputSuccess, DataCenterModelOutputFailure> for DataCenterModel where
    T: Value<'a>, 

fn to(
    &self,
    _: DataCenterOfflineInput
) -> SimplifiedSmoothedConvexOptimization<'a, T, DataCenterModelOutputSuccess, DataCenterModelOutputFailure>

Generates an offline problem instance given some $input$ (with certainty).

fn update(
    &self,
    o: &mut Online<SimplifiedSmoothedConvexOptimization<'a, T, DataCenterModelOutputSuccess, DataCenterModelOutputFailure>>,
    _: DataCenterOnlineInput
)

Performs an online update of the given problem instance $o$ with some $input$ (which may be uncertain).

impl<'a, T, C, D> Problem<T, C, D> for SimplifiedSmoothedConvexOptimization<'a, T, C, D> where
    T: Value<'a>,
    C: ModelOutputSuccess,
    D: ModelOutputFailure, 

fn hit_cost(&self, t: i32, x: Config<T>) -> Cost<C, D>

Hitting cost.

fn movement(&self, prev_x: Config<T>, x: Config<T>, inverted: bool) -> N64

Movement cost.

fn objective_function<'a>(&self, xs: &Schedule<T>) -> Result<Cost<C, D>> where
    T: Value<'a>, 

Objective function. Calculates the cost of a schedule.

fn inverted_objective_function<'a>(
    &self,
    xs: &Schedule<T>
) -> Result<Cost<C, D>> where
    T: Value<'a>, 

Inverted Objective function. Calculates the cost of a schedule. Pays the switching cost for powering down rather than powering up.

fn alpha_unfair_objective_function<'a>(
    &self,
    xs: &Schedule<T>,
    alpha: f64
) -> Result<Cost<C, D>> where
    T: Value<'a>, 

$\alpha$-unfair Objective function. Calculates the cost of a schedule.

fn objective_function_with_default<'a>(
    &self,
    xs: &Schedule<T>,
    default: &Config<T>
) -> Result<Cost<C, D>> where
    T: Value<'a>, 

Objective function starting from an initial configuration other than $\mathbf{0}$.

fn _objective_function_with_default<'a>(
    &self,
    xs: &Schedule<T>,
    default: &Config<T>,
    alpha: f64,
    inverted: bool
) -> Result<Cost<C, D>> where
    T: Value<'a>, 

fn total_movement<'a>(&self, xs: &Schedule<T>, inverted: bool) -> Result<N64> where
    T: Value<'a>, 

Movement in the decision space.

fn _default_config<'a>(&self) -> Config<T> where
    T: Value<'a>, 

impl<'a, T, C, D> ResettableProblem<'a, T> for SimplifiedSmoothedConvexOptimization<'a, T, C, D> where
    T: Value<'a>,
    C: ModelOutputSuccess,
    D: ModelOutputFailure, 

fn reset(
    &'a self,
    t_start: i32
) -> SimplifiedSmoothedConvexOptimization<'a, T, C, D>

Shifts problem instance to some new initial time $t_start$.

impl<'a, T, C, D> VerifiableProblem for SimplifiedSmoothedConvexOptimization<'a, T, C, D> where
    T: Value<'a>, 

fn verify(&self) -> Result<()>