Mon, 06 Jan 2025 20:29:25 -0500
More Serialize / Deserialize / Debug derives
/*! Norms, projections, etc. */ use serde::{Serialize, Deserialize}; use std::marker::PhantomData; use crate::types::*; use crate::euclidean::*; use crate::mapping::{Mapping, Space, Instance}; // // Abstract norms // #[derive(Copy,Clone,Debug, Serialize, Deserialize)] /// Helper structure to convert a [`NormExponent`] into a [`Mapping`] pub struct NormMapping<F : Float, E : NormExponent>{ pub(crate) exponent : E, #[serde(skip)] _phantoms : PhantomData<F> } /// An exponent for norms. /// // Just a collection of desirable attributes for a marker type pub trait NormExponent : Copy + Send + Sync + 'static { /// Return the norm as a mappin fn as_mapping<F : Float>(self) -> NormMapping<F, Self> { NormMapping{ exponent : self, _phantoms : PhantomData } } } /// Exponent type for the 1-[`Norm`]. #[derive(Copy,Debug,Clone,Serialize,Deserialize,Eq,PartialEq)] pub struct L1; impl NormExponent for L1 {} /// Exponent type for the 2-[`Norm`]. #[derive(Copy,Debug,Clone,Serialize,Deserialize,Eq,PartialEq)] pub struct L2; impl NormExponent for L2 {} /// Exponent type for the ∞-[`Norm`]. #[derive(Copy,Debug,Clone,Serialize,Deserialize,Eq,PartialEq)] pub struct Linfinity; impl NormExponent for Linfinity {} /// Exponent type for 2,1-[`Norm`]. /// (1-norm over a domain Ω, 2-norm of a vector at each point of the domain.) #[derive(Copy,Debug,Clone,Serialize,Deserialize,Eq,PartialEq)] pub struct L21; impl NormExponent for L21 {} /// Norms for pairs (a, b). ‖(a,b)‖ = ‖(‖a‖_A, ‖b‖_B)‖_J /// For use with [`crate::direct_product::Pair`] #[derive(Copy,Debug,Clone,Serialize,Deserialize,Eq,PartialEq)] pub struct PairNorm<A, B, J>(pub A, pub B, pub J); impl<A, B, J> NormExponent for PairNorm<A, B, J> where A : NormExponent, B : NormExponent, J : NormExponent {} /// A Huber/Moreau–Yosida smoothed [`L1`] norm. (Not a norm itself.) /// /// The parameter γ of this type is the smoothing factor. Zero means no smoothing, and higher /// values more smoothing. Behaviour with γ < 0 is undefined. #[derive(Copy,Debug,Clone,Serialize,Deserialize,Eq,PartialEq)] pub struct HuberL1<F : Float>(pub F); impl<F : Float> NormExponent for HuberL1<F> {} /// A Huber/Moreau–Yosida smoothed [`L21`] norm. (Not a norm itself.) /// /// The parameter γ of this type is the smoothing factor. Zero means no smoothing, and higher /// values more smoothing. Behaviour with γ < 0 is undefined. #[derive(Copy,Debug,Clone,Serialize,Deserialize,Eq,PartialEq)] pub struct HuberL21<F : Float>(pub F); impl<F : Float> NormExponent for HuberL21<F> {} /// A normed space (type) with exponent or other type `Exponent` for the norm. /// /// Use as /// ``` /// # use alg_tools::norms::{Norm, L1, L2, Linfinity}; /// # use alg_tools::loc::Loc; /// let x = Loc([1.0, 2.0, 3.0]); /// /// println!("{}, {} {}", x.norm(L1), x.norm(L2), x.norm(Linfinity)) /// ``` pub trait Norm<F : Num, Exponent : NormExponent> { /// Calculate the norm. fn norm(&self, _p : Exponent) -> F; } /// Indicates that the `Self`-[`Norm`] is dominated by the `Exponent`-`Norm` on the space /// `Elem` with the corresponding field `F`. pub trait Dominated<F : Num, Exponent : NormExponent, Elem> { /// Indicates the factor $c$ for the inequality $‖x‖ ≤ C ‖x‖_p$. fn norm_factor(&self, p : Exponent) -> F; /// Given a norm-value $‖x‖_p$, calculates $C‖x‖_p$ such that $‖x‖ ≤ C‖x‖_p$ #[inline] fn from_norm(&self, p_norm : F, p : Exponent) -> F { p_norm * self.norm_factor(p) } } /// Trait for distances with respect to a norm. pub trait Dist<F : Num, Exponent : NormExponent> : Norm<F, Exponent> + Space { /// Calculate the distance fn dist<I : Instance<Self>>(&self, other : I, _p : Exponent) -> F; } /// Trait for Euclidean projections to the `Exponent`-[`Norm`]-ball. /// /// Use as /// ``` /// # use alg_tools::norms::{Projection, L2, Linfinity}; /// # use alg_tools::loc::Loc; /// let x = Loc([1.0, 2.0, 3.0]); /// /// println!("{:?}, {:?}", x.proj_ball(1.0, L2), x.proj_ball(0.5, Linfinity)); /// ``` pub trait Projection<F : Num, Exponent : NormExponent> : Norm<F, Exponent> + Sized where F : Float { /// Projection of `self` to the `q`-norm-ball of radius ρ. fn proj_ball(mut self, ρ : F, q : Exponent) -> Self { self.proj_ball_mut(ρ, q); self } /// In-place projection of `self` to the `q`-norm-ball of radius ρ. fn proj_ball_mut(&mut self, ρ : F, q : Exponent); } /*impl<F : Float, E : Euclidean<F>> Norm<F, L2> for E { #[inline] fn norm(&self, _p : L2) -> F { self.norm2() } fn dist(&self, other : &Self, _p : L2) -> F { self.dist2(other) } }*/ impl<F : Float, E : Euclidean<F> + Norm<F, L2>> Projection<F, L2> for E { #[inline] fn proj_ball(self, ρ : F, _p : L2) -> Self { self.proj_ball2(ρ) } #[inline] fn proj_ball_mut(&mut self, ρ : F, _p : L2) { self.proj_ball2_mut(ρ) } } impl<F : Float> HuberL1<F> { fn apply(self, xnsq : F) -> F { let HuberL1(γ) = self; let xn = xnsq.sqrt(); if γ == F::ZERO { xn } else { if xn > γ { xn-γ / F::TWO } else if xn<(-γ) { -xn-γ / F::TWO } else { xnsq / (F::TWO * γ) } } } } impl<F : Float, E : Euclidean<F>> Norm<F, HuberL1<F>> for E { fn norm(&self, huber : HuberL1<F>) -> F { huber.apply(self.norm2_squared()) } } impl<F : Float, E : Euclidean<F>> Dist<F, HuberL1<F>> for E { fn dist<I : Instance<Self>>(&self, other : I, huber : HuberL1<F>) -> F { huber.apply(self.dist2_squared(other)) } } // impl<F : Float, E : Norm<F, L2>> Norm<F, L21> for Vec<E> { // fn norm(&self, _l21 : L21) -> F { // self.iter().map(|e| e.norm(L2)).sum() // } // } // impl<F : Float, E : Dist<F, L2>> Dist<F, L21> for Vec<E> { // fn dist<I : Instance<Self>>(&self, other : I, _l21 : L21) -> F { // self.iter().zip(other.iter()).map(|(e, g)| e.dist(g, L2)).sum() // } // } impl<E, F, Domain> Mapping<Domain> for NormMapping<F, E> where F : Float, E : NormExponent, Domain : Space + Norm<F, E>, { type Codomain = F; #[inline] fn apply<I : Instance<Domain>>(&self, x : I) -> F { x.eval(|r| r.norm(self.exponent)) } } pub trait Normed<F : Num = f64> : Space + Norm<F, Self::NormExp> { type NormExp : NormExponent; fn norm_exponent(&self) -> Self::NormExp; #[inline] fn norm_(&self) -> F { self.norm(self.norm_exponent()) } // fn similar_origin(&self) -> Self; fn is_zero(&self) -> bool; } pub trait HasDual<F : Num = f64> : Normed<F> { type DualSpace : Normed<F>; } /// Automatically implemented trait for reflexive spaces pub trait Reflexive<F : Num = f64> : HasDual<F> where Self::DualSpace : HasDual<F, DualSpace = Self> { } impl<F : Num, X : HasDual<F>> Reflexive<F> for X where X::DualSpace : HasDual<F, DualSpace = X> { } pub trait HasDualExponent : NormExponent { type DualExp : NormExponent; fn dual_exponent(&self) -> Self::DualExp; } impl HasDualExponent for L2 { type DualExp = L2; #[inline] fn dual_exponent(&self) -> Self::DualExp { L2 } } impl HasDualExponent for L1 { type DualExp = Linfinity; #[inline] fn dual_exponent(&self) -> Self::DualExp { Linfinity } } impl HasDualExponent for Linfinity { type DualExp = L1; #[inline] fn dual_exponent(&self) -> Self::DualExp { L1 } } #[macro_export] macro_rules! impl_weighted_norm { ($exponent : ty) => { impl<C, F, D> Norm<F, Weighted<$exponent, C>> for D where F : Float, D : Norm<F, $exponent>, C : Constant<Type = F>, { fn norm(&self, e : Weighted<$exponent, C>) -> F { let v = e.weight.value(); assert!(v > F::ZERO); v * self.norm(e.base_fn) } } impl<C : Constant> NormExponent for Weighted<$exponent, C> {} impl<C : Constant> HasDualExponent for Weighted<$exponent, C> where $exponent : HasDualExponent { type DualExp = Weighted<<$exponent as HasDualExponent>::DualExp, C::Type>; fn dual_exponent(&self) -> Self::DualExp { Weighted { weight : C::Type::ONE / self.weight.value(), base_fn : self.base_fn.dual_exponent() } } } impl<C, F, T> Projection<F, Weighted<$exponent , C>> for T where T : Projection<F, $exponent >, F : Float, C : Constant<Type = F>, { fn proj_ball(self, ρ : F, q : Weighted<$exponent , C>) -> Self { self.proj_ball(ρ / q.weight.value(), q.base_fn) } fn proj_ball_mut(&mut self, ρ : F, q : Weighted<$exponent , C>) { self.proj_ball_mut(ρ / q.weight.value(), q.base_fn) } } } } //impl_weighted_norm!(L1); //impl_weighted_norm!(L2); //impl_weighted_norm!(Linfinity);