1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143

//! FBS Algorithm
//!
use crate::{
    constraints,
    core::{
        fbs::fbs_engine::FBSEngine, fbs::FBSCache, AlgorithmEngine, ExitStatus, Optimizer, Problem,
        SolverStatus,
    },
    matrix_operations, FunctionCallResult, SolverError,
};
use std::time;

const MAX_ITER: usize = 100_usize;

/// Optimiser using forward-backward splitting iterations (projected gradient)
///
/// Note that an `FBSOptimizer` holds a reference to an instance of `FBSEngine`
/// which needs to be created externally. A mutable reference to that `FBSEgnine`
/// is provided to the optimizer.
///
/// The `FBSEngine` is supposed to be updated whenever you need to solve
/// a different optimization problem.
///
///
pub struct FBSOptimizer<'a, GradientType, ConstraintType, CostType>
where
    GradientType: Fn(&[f64], &mut [f64]) -> FunctionCallResult,
    CostType: Fn(&[f64], &mut f64) -> FunctionCallResult,
    ConstraintType: constraints::Constraint,
{
    fbs_engine: FBSEngine<'a, GradientType, ConstraintType, CostType>,
    max_iter: usize,
    max_duration: Option<time::Duration>,
}

impl<'a, GradientType, ConstraintType, CostType>
    FBSOptimizer<'a, GradientType, ConstraintType, CostType>
where
    GradientType: Fn(&[f64], &mut [f64]) -> FunctionCallResult,
    CostType: Fn(&[f64], &mut f64) -> FunctionCallResult,
    ConstraintType: constraints::Constraint,
{
    /// Constructs a new instance of `FBSOptimizer`
    ///
    /// ## Arguments
    ///
    /// - `problem`: problem definition
    /// - `cache`: instance of `FBSCache`
    pub fn new(
        problem: Problem<'a, GradientType, ConstraintType, CostType>,
        cache: &'a mut FBSCache,
    ) -> Self {
        FBSOptimizer {
            fbs_engine: FBSEngine::new(problem, cache),
            max_iter: MAX_ITER,
            max_duration: None,
        }
    }

    /// Sets the tolerance
    ///
    /// ## Panics
    ///
    /// The method panics if the specified tolerance is not positive
    pub fn with_tolerance(
        self,
        tolerance: f64,
    ) -> FBSOptimizer<'a, GradientType, ConstraintType, CostType> {
        assert!(tolerance > 0.0);

        self.fbs_engine.cache.tolerance = tolerance;
        self
    }

    /// Sets the maximum number of iterations
    pub fn with_max_iter(
        mut self,
        max_iter: usize,
    ) -> FBSOptimizer<'a, GradientType, ConstraintType, CostType> {
        self.max_iter = max_iter;
        self
    }

    /// Sets the maximum number of iterations
    pub fn with_max_duration(
        mut self,
        max_duration: time::Duration,
    ) -> FBSOptimizer<'a, GradientType, ConstraintType, CostType> {
        self.max_duration = Some(max_duration);
        self
    }
}

impl<'life, GradientType, ConstraintType, CostType> Optimizer
    for FBSOptimizer<'life, GradientType, ConstraintType, CostType>
where
    GradientType: Fn(&[f64], &mut [f64]) -> FunctionCallResult + 'life,
    CostType: Fn(&[f64], &mut f64) -> FunctionCallResult + 'life,
    ConstraintType: constraints::Constraint + 'life,
{
    fn solve(&mut self, u: &mut [f64]) -> Result<SolverStatus, SolverError> {
        let now = instant::Instant::now();

        // Initialize - propagate error upstream, if any
        self.fbs_engine.init(u)?;

        let mut num_iter: usize = 0;
        let mut step_flag = self.fbs_engine.step(u)?;

        if let Some(dur) = self.max_duration {
            while step_flag && num_iter < self.max_iter && dur <= now.elapsed() {
                num_iter += 1;
                step_flag = self.fbs_engine.step(u)?
            }
        } else {
            while step_flag && num_iter < self.max_iter {
                num_iter += 1;
                step_flag = self.fbs_engine.step(u)?
            }
        }

        // cost at the solution [propagate error upstream]
        let mut cost_value: f64 = 0.0;
        (self.fbs_engine.problem.cost)(u, &mut cost_value)?;

        if !matrix_operations::is_finite(u) || !cost_value.is_finite() {
            return Err(SolverError::NotFiniteComputation);
        }

        // export solution status
        Ok(SolverStatus::new(
            if num_iter < self.max_iter {
                ExitStatus::Converged
            } else {
                ExitStatus::NotConvergedIterations
            },
            num_iter,
            now.elapsed(),
            self.fbs_engine.cache.norm_fpr,
            cost_value,
        ))
    }
}