Struct OptObserverVec 

pub struct OptObserverVec { /* private fields */ }

Collection of observers for optimization monitoring.

This struct manages a vector of observers and provides a convenient notify() method to call all observers at once. Optimizers use this internally to manage their observers.

Usage

Typically you don’t create this directly - use the add_observer() method on optimizers. However, you can use it for custom optimization algorithms:

use apex_solver::observers::{OptObserver, OptObserverVec};
use apex_solver::core::problem::VariableEnum;
use std::collections::HashMap;

struct MyOptimizer {
    observers: OptObserverVec,
    // ... other fields ...
}

impl MyOptimizer {
    fn step(&mut self, values: &HashMap<String, VariableEnum>, iteration: usize) {
        // ... optimization logic ...

        // Notify all observers
        self.observers.notify(values, iteration);
    }
}

Implementations

impl OptObserverVec

pub fn new() -> Self

Create a new empty observer collection.

pub fn add(&mut self, observer: impl OptObserver + 'static)

Add an observer to the collection.

The observer will be called at each optimization iteration in the order it was added.

Arguments

• observer - Any type implementing OptObserver

Examples

use apex_solver::observers::{OptObserver, OptObserverVec};
use apex_solver::core::problem::VariableEnum;
use std::collections::HashMap;

struct MyObserver;
impl OptObserver for MyObserver {
    fn on_step(&self, _values: &HashMap<String, VariableEnum>, _iteration: usize) {
        // Handle optimization step
    }
}

let mut observers = OptObserverVec::new();
observers.add(MyObserver);

pub fn set_iteration_metrics( &self, cost: f64, gradient_norm: f64, damping: Option<f64>, step_norm: f64, step_quality: Option<f64>, )

Set iteration metrics for all observers.

Calls set_iteration_metrics() on each registered observer. This should be called before notify() to provide optimization metrics.

Arguments

• cost - Current cost function value
• gradient_norm - L2 norm of the gradient vector
• damping - Damping parameter (may be None)
• step_norm - L2 norm of the parameter update step
• step_quality - Step quality metric (may be None)

pub fn set_matrix_data( &self, hessian: Option<SparseColMat<usize, f64>>, gradient: Option<Mat<f64>>, )

Set matrix data for all observers.

Calls set_matrix_data() on each registered observer. This should be called before notify() to provide matrix data for visualization.

Arguments

• hessian - Sparse Hessian matrix
• gradient - Gradient vector

pub fn notify(&self, values: &HashMap<String, VariableEnum>, iteration: usize)

Notify all observers with current optimization state.

Calls on_step() on each registered observer in order. If no observers are registered, this is a no-op with zero overhead.

Arguments

• values - Current variable values
• iteration - Current iteration number

Examples

use apex_solver::observers::OptObserverVec;
use std::collections::HashMap;

let observers = OptObserverVec::new();
let values = HashMap::new();

// Notify all observers (safe even if empty)
observers.notify(&values, 0);

pub fn notify_complete( &self, values: &HashMap<String, VariableEnum>, iterations: usize, )

Notify all observers that optimization is complete.

Calls on_optimization_complete() on each registered observer. This should be called once at the end of optimization, after all iterations are done.

Arguments

• values - Final optimized variable values
• iterations - Total number of iterations performed

Examples

use apex_solver::observers::OptObserverVec;
use std::collections::HashMap;

let observers = OptObserverVec::new();
let values = HashMap::new();

// Notify all observers that optimization is complete
observers.notify_complete(&values, 50);

pub fn is_empty(&self) -> bool

Check if any observers are registered.

Useful for conditional logic or debugging.

pub fn len(&self) -> usize

Get the number of registered observers.

Trait Implementations

impl Default for OptObserverVec

fn default() -> OptObserverVec

Returns the “default value” for a type.