Mon, 17 Feb 2025 13:54:53 -0500
Merge dev to default
32 | 1 | /*! |
2 | Basid definitions for data terms | |
3 | */ | |
4 | ||
5 | use numeric_literals::replace_float_literals; | |
6 | ||
7 | use alg_tools::euclidean::Euclidean; | |
8 | use alg_tools::linops::GEMV; | |
9 | pub use alg_tools::norms::L1; | |
10 | use alg_tools::norms::Norm; | |
35 | 11 | use alg_tools::instance::{Instance, Space}; |
32 | 12 | |
13 | use crate::types::*; | |
14 | pub use crate::types::L2Squared; | |
35 | 15 | use crate::measures::RNDM; |
32 | 16 | |
17 | /// Calculates the residual $Aμ-b$. | |
18 | #[replace_float_literals(F::cast_from(literal))] | |
19 | pub(crate) fn calculate_residual< | |
35 | 20 | X : Space, |
21 | I : Instance<X>, | |
32 | 22 | F : Float, |
23 | V : Euclidean<F> + Clone, | |
35 | 24 | A : GEMV<F, X, Codomain = V>, |
32 | 25 | >( |
35 | 26 | μ : I, |
32 | 27 | opA : &A, |
28 | b : &V | |
29 | ) -> V { | |
30 | let mut r = b.clone(); | |
31 | opA.gemv(&mut r, 1.0, μ, -1.0); | |
32 | r | |
33 | } | |
34 | ||
35 | /// Calculates the residual $A(μ+μ_delta)-b$. | |
36 | #[replace_float_literals(F::cast_from(literal))] | |
37 | pub(crate) fn calculate_residual2< | |
38 | F : Float, | |
35 | 39 | X : Space, |
40 | I : Instance<X>, | |
41 | J : Instance<X>, | |
32 | 42 | V : Euclidean<F> + Clone, |
35 | 43 | A : GEMV<F, X, Codomain = V>, |
32 | 44 | >( |
35 | 45 | μ : I, |
46 | μ_delta : J, | |
32 | 47 | opA : &A, |
48 | b : &V | |
49 | ) -> V { | |
50 | let mut r = b.clone(); | |
51 | opA.gemv(&mut r, 1.0, μ, -1.0); | |
34
efa60bc4f743
Radon FB + sliding improvements
Tuomo Valkonen <tuomov@iki.fi>
parents:
32
diff
changeset
|
52 | opA.gemv(&mut r, 1.0, μ_delta, 1.0); |
32 | 53 | r |
54 | } | |
55 | ||
56 | ||
57 | /// Trait for data terms | |
58 | #[replace_float_literals(F::cast_from(literal))] | |
59 | pub trait DataTerm<F : Float, V, const N : usize> { | |
60 | /// Calculates $F(y)$, where $F$ is the data fidelity. | |
61 | fn calculate_fit(&self, _residual : &V) -> F; | |
62 | ||
63 | /// Calculates $F(Aμ-b)$, where $F$ is the data fidelity. | |
35 | 64 | fn calculate_fit_op<I, A : GEMV<F, RNDM<F, N>, Codomain = V>>( |
32 | 65 | &self, |
35 | 66 | μ : I, |
32 | 67 | opA : &A, |
68 | b : &V | |
69 | ) -> F | |
35 | 70 | where |
71 | V : Euclidean<F> + Clone, | |
72 | I : Instance<RNDM<F, N>>, | |
73 | { | |
74 | let r = calculate_residual(μ, opA, b); | |
32 | 75 | self.calculate_fit(&r) |
76 | } | |
77 | } | |
78 | ||
79 | impl<F : Float, V : Euclidean<F>, const N : usize> | |
80 | DataTerm<F, V, N> | |
81 | for L2Squared { | |
82 | fn calculate_fit(&self, residual : &V) -> F { | |
83 | residual.norm2_squared_div2() | |
84 | } | |
85 | } | |
86 | ||
87 | ||
88 | impl<F : Float, V : Euclidean<F> + Norm<F, L1>, const N : usize> | |
89 | DataTerm<F, V, N> | |
90 | for L1 { | |
91 | fn calculate_fit(&self, residual : &V) -> F { | |
92 | residual.norm(L1) | |
93 | } | |
94 | } |