Mercurial > repos > alg_tools / changeset

--- a/src/discrete_gradient.rs	Wed Apr 30 01:06:25 2025 -0500
+++ b/src/discrete_gradient.rs	Wed Apr 30 16:39:01 2025 -0500
@@ -1,14 +1,13 @@
 /*!
 Simple disrete gradient operators
  */
+use crate::error::DynResult;
+use crate::instance::Instance;
+use crate::linops::{Adjointable, BoundedLinear, Linear, Mapping, GEMV};
+use crate::norms::{Norm, L2};
+use crate::types::Float;
+use nalgebra::{DVector, Dyn, Matrix, Storage, StorageMut, U1};
 use numeric_literals::replace_float_literals;
-use nalgebra::{
-    DVector, Matrix, U1, Storage, StorageMut, Dyn
-};
-use crate::types::Float;
-use crate::instance::Instance;
-use crate::linops::{Mapping, Linear, BoundedLinear, Adjointable, GEMV};
-use crate::norms::{Norm, L2};
 
 #[derive(Copy, Clone, Debug)]
 /// Forward differences with Neumann boundary conditions
@@ -27,26 +26,17 @@
 pub struct BackwardNeumann;
 
 /// Finite differences gradient
-pub struct Grad<
-    F : Float + nalgebra::RealField,
-    B : Discretisation<F>,
-    const N : usize
-> {
-    dims : [usize; N],
-    h : F, // may be negative to implement adjoints!
-    discretisation : B,
+pub struct Grad<F: Float + nalgebra::RealField, B: Discretisation<F>, const N: usize> {
+    dims: [usize; N],
+    h: F, // may be negative to implement adjoints!
+    discretisation: B,
 }
 
-
 /// Finite differences divergence
-pub struct Div<
-    F : Float + nalgebra::RealField,
-    B : Discretisation<F>,
-    const N : usize
-> {
-    dims : [usize; N],
-    h : F, // may be negative to implement adjoints!
-    discretisation : B,
+pub struct Div<F: Float + nalgebra::RealField, B: Discretisation<F>, const N: usize> {
+    dims: [usize; N],
+    h: F, // may be negative to implement adjoints!
+    discretisation: B,
 }
 
 /// Internal: classification of a point in a 1D discretisation
@@ -62,53 +52,53 @@
 use DiscretisationOrInterior::*;
 
 /// Trait for different discretisations
-pub trait Discretisation<F : Float + nalgebra::RealField> : Copy {
+pub trait Discretisation<F: Float + nalgebra::RealField>: Copy {
     /// Opposite discretisation, appropriate for adjoints with negated cell width.
-    type Opposite : Discretisation<F>;
+    type Opposite: Discretisation<F>;
 
     /// Add to appropiate index of `v` (as determined by `b`) the appropriate difference
     /// of `x` with cell width `h`.
     fn add_diff_mut<SMut, S>(
         &self,
-        v : &mut Matrix<F, Dyn, U1, SMut>,
-        x : &Matrix<F, Dyn, U1, S>,
-        α : F,
-        b : DiscretisationOrInterior,
+        v: &mut Matrix<F, Dyn, U1, SMut>,
+        x: &Matrix<F, Dyn, U1, S>,
+        α: F,
+        b: DiscretisationOrInterior,
     ) where
-        SMut : StorageMut<F, Dyn, U1>,
-        S : Storage<F, Dyn, U1>;
+        SMut: StorageMut<F, Dyn, U1>,
+        S: Storage<F, Dyn, U1>;
 
     /// Give the opposite discretisation, appropriate for adjoints with negated `h`.
     fn opposite(&self) -> Self::Opposite;
 
     /// Bound for the corresponding operator norm.
     #[replace_float_literals(F::cast_from(literal))]
-    fn opnorm_bound(&self, h : F) -> F {
+    fn opnorm_bound(&self, h: F) -> DynResult<F> {
         // See: Chambolle, “An Algorithm for Total Variation Minimization and Applications”.
         // Ok for forward and backward differences.
         //
         // Fuck nalgebra for polluting everything with its own shit.
-        num_traits::Float::sqrt(8.0) / h
+        Ok(num_traits::Float::sqrt(8.0) / h)
     }
 }
 
-impl<F : Float + nalgebra::RealField> Discretisation<F> for ForwardNeumann {
+impl<F: Float + nalgebra::RealField> Discretisation<F> for ForwardNeumann {
     type Opposite = BackwardDirichlet;
 
     #[inline]
     fn add_diff_mut<SMut, S>(
         &self,
-        v : &mut Matrix<F, Dyn, U1, SMut>,
-        x : &Matrix<F, Dyn, U1, S>,
-        α : F,
-        b : DiscretisationOrInterior,
+        v: &mut Matrix<F, Dyn, U1, SMut>,
+        x: &Matrix<F, Dyn, U1, S>,
+        α: F,
+        b: DiscretisationOrInterior,
     ) where
-        SMut : StorageMut<F, Dyn, U1>,
-        S : Storage<F, Dyn, U1>
+        SMut: StorageMut<F, Dyn, U1>,
+        S: Storage<F, Dyn, U1>,
     {
         match b {
-            Interior(c, (_, f)) | LeftBoundary(c, f) => { v[c] += (x[f] - x[c]) * α },
-            RightBoundary(_c, _b) => { },
+            Interior(c, (_, f)) | LeftBoundary(c, f) => v[c] += (x[f] - x[c]) * α,
+            RightBoundary(_c, _b) => {}
         }
     }
 
@@ -118,23 +108,23 @@
     }
 }
 
-impl<F : Float + nalgebra::RealField> Discretisation<F> for BackwardNeumann {
+impl<F: Float + nalgebra::RealField> Discretisation<F> for BackwardNeumann {
     type Opposite = ForwardDirichlet;
 
     #[inline]
     fn add_diff_mut<SMut, S>(
         &self,
-        v : &mut Matrix<F, Dyn, U1, SMut>,
-        x : &Matrix<F, Dyn, U1, S>,
-        α : F,
-        b : DiscretisationOrInterior,
+        v: &mut Matrix<F, Dyn, U1, SMut>,
+        x: &Matrix<F, Dyn, U1, S>,
+        α: F,
+        b: DiscretisationOrInterior,
     ) where
-        SMut : StorageMut<F, Dyn, U1>,
-        S : Storage<F, Dyn, U1>
+        SMut: StorageMut<F, Dyn, U1>,
+        S: Storage<F, Dyn, U1>,
     {
         match b {
-            Interior(c, (b, _)) | RightBoundary(c, b) => { v[c] += (x[c] - x[b]) * α },
-            LeftBoundary(_c, _f) => { },
+            Interior(c, (b, _)) | RightBoundary(c, b) => v[c] += (x[c] - x[b]) * α,
+            LeftBoundary(_c, _f) => {}
         }
     }
 
@@ -144,24 +134,24 @@
     }
 }
 
-impl<F : Float + nalgebra::RealField> Discretisation<F> for BackwardDirichlet {
+impl<F: Float + nalgebra::RealField> Discretisation<F> for BackwardDirichlet {
     type Opposite = ForwardNeumann;
 
     #[inline]
     fn add_diff_mut<SMut, S>(
         &self,
-        v : &mut Matrix<F, Dyn, U1, SMut>,
-        x : &Matrix<F, Dyn, U1, S>,
-        α : F,
-        b : DiscretisationOrInterior,
+        v: &mut Matrix<F, Dyn, U1, SMut>,
+        x: &Matrix<F, Dyn, U1, S>,
+        α: F,
+        b: DiscretisationOrInterior,
     ) where
-        SMut : StorageMut<F, Dyn, U1>,
-        S : Storage<F, Dyn, U1>
+        SMut: StorageMut<F, Dyn, U1>,
+        S: Storage<F, Dyn, U1>,
     {
         match b {
-            Interior(c, (b, _f)) => { v[c] += (x[c] - x[b]) * α },
-            LeftBoundary(c, _f) => { v[c] += x[c] * α },
-            RightBoundary(c, b) => { v[c] -= x[b] * α },
+            Interior(c, (b, _f)) => v[c] += (x[c] - x[b]) * α,
+            LeftBoundary(c, _f) => v[c] += x[c] * α,
+            RightBoundary(c, b) => v[c] -= x[b] * α,
         }
     }
 
@@ -171,24 +161,24 @@
     }
 }
 
-impl<F : Float + nalgebra::RealField> Discretisation<F> for ForwardDirichlet {
+impl<F: Float + nalgebra::RealField> Discretisation<F> for ForwardDirichlet {
     type Opposite = BackwardNeumann;
 
     #[inline]
     fn add_diff_mut<SMut, S>(
         &self,
-        v : &mut Matrix<F, Dyn, U1, SMut>,
-        x : &Matrix<F, Dyn, U1, S>,
-        α : F,
-        b : DiscretisationOrInterior,
+        v: &mut Matrix<F, Dyn, U1, SMut>,
+        x: &Matrix<F, Dyn, U1, S>,
+        α: F,
+        b: DiscretisationOrInterior,
     ) where
-        SMut : StorageMut<F, Dyn, U1>,
-        S : Storage<F, Dyn, U1>
+        SMut: StorageMut<F, Dyn, U1>,
+        S: Storage<F, Dyn, U1>,
     {
         match b {
-            Interior(c, (_b, f)) => { v[c] += (x[f] - x[c]) * α },
-            LeftBoundary(c, f) => { v[c] += x[f] * α },
-            RightBoundary(c, _b) => { v[c] -= x[c] * α },
+            Interior(c, (_b, f)) => v[c] += (x[f] - x[c]) * α,
+            LeftBoundary(c, f) => v[c] += x[f] * α,
+            RightBoundary(c, _b) => v[c] -= x[c] * α,
         }
     }
 
@@ -198,30 +188,37 @@
     }
 }
 
-struct Iter<'a, const N : usize> {
+struct Iter<'a, const N: usize> {
     /// Dimensions
-    dims : &'a [usize; N],
+    dims: &'a [usize; N],
     /// Dimension along which to calculate differences
-    d : usize,
+    d: usize,
     /// Stride along coordinate d
-    d_stride : usize,
+    d_stride: usize,
     /// Cartesian indices
-    i : [usize; N],
+    i: [usize; N],
     /// Linear index
-    k : usize,
+    k: usize,
     /// Maximal linear index
-    len : usize
+    len: usize,
 }
 
-impl<'a,  const N : usize> Iter<'a, N> {
-    fn new(dims : &'a [usize; N], d : usize) -> Self {
+impl<'a, const N: usize> Iter<'a, N> {
+    fn new(dims: &'a [usize; N], d: usize) -> Self {
         let d_stride = dims[0..d].iter().product::<usize>();
         let len = dims.iter().product::<usize>();
-        Iter{ dims, d, d_stride, i : [0; N], k : 0, len }
+        Iter {
+            dims,
+            d,
+            d_stride,
+            i: [0; N],
+            k: 0,
+            len,
+        }
     }
 }
 
-impl<'a, const N : usize> Iterator for Iter<'a, N> {
+impl<'a, const N: usize> Iterator for Iter<'a, N> {
     type Item = DiscretisationOrInterior;
     fn next(&mut self) -> Option<Self::Item> {
         let res = if self.k >= self.len {
@@ -243,7 +240,7 @@
         for j in 0..N {
             if self.i[j] + 1 < self.dims[j] {
                 self.i[j] += 1;
-                break
+                break;
             }
             self.i[j] = 0
         }
@@ -251,14 +248,13 @@
     }
 }
 
-impl<F, B, const N : usize> Mapping<DVector<F>>
-for Grad<F, B, N>
+impl<F, B, const N: usize> Mapping<DVector<F>> for Grad<F, B, N>
 where
-    B : Discretisation<F>,
-    F : Float + nalgebra::RealField,
+    B: Discretisation<F>,
+    F: Float + nalgebra::RealField,
 {
     type Codomain = DVector<F>;
-    fn apply<I : Instance<DVector<F>>>(&self, i : I) -> DVector<F> {
+    fn apply<I: Instance<DVector<F>>>(&self, i: I) -> DVector<F> {
         let mut y = DVector::zeros(N * self.len());
         self.apply_add(&mut y, i);
         y
@@ -266,15 +262,12 @@
 }
 
 #[replace_float_literals(F::cast_from(literal))]
-impl<F, B, const N : usize> GEMV<F, DVector<F>>
-for Grad<F, B, N>
+impl<F, B, const N: usize> GEMV<F, DVector<F>> for Grad<F, B, N>
 where
-    B : Discretisation<F>,
-    F : Float + nalgebra::RealField,
+    B: Discretisation<F>,
+    F: Float + nalgebra::RealField,
 {
-    fn gemv<I : Instance<DVector<F>>>(
-        &self, y : &mut DVector<F>, α : F, i : I, β : F
-    ) {
+    fn gemv<I: Instance<DVector<F>>>(&self, y: &mut DVector<F>, α: F, i: I, β: F) {
         if β == 0.0 {
             y.as_mut_slice().iter_mut().for_each(|x| *x = 0.0);
         } else if β != 1.0 {
@@ -286,23 +279,22 @@
         i.eval(|x| {
             assert_eq!(x.len(), m);
             for d in 0..N {
-                let mut v = y.generic_view_mut((d*m, 0), (Dyn(m), U1));
+                let mut v = y.generic_view_mut((d * m, 0), (Dyn(m), U1));
                 for b in Iter::new(&self.dims, d) {
-                    self.discretisation.add_diff_mut(&mut v, x, α/h, b)
+                    self.discretisation.add_diff_mut(&mut v, x, α / h, b)
                 }
             }
         })
     }
 }
 
-impl<F, B, const N : usize> Mapping<DVector<F>>
-for Div<F, B, N>
+impl<F, B, const N: usize> Mapping<DVector<F>> for Div<F, B, N>
 where
-    B : Discretisation<F>,
-    F : Float + nalgebra::RealField,
+    B: Discretisation<F>,
+    F: Float + nalgebra::RealField,
 {
     type Codomain = DVector<F>;
-    fn apply<I : Instance<DVector<F>>>(&self, i : I) -> DVector<F> {
+    fn apply<I: Instance<DVector<F>>>(&self, i: I) -> DVector<F> {
         let mut y = DVector::zeros(self.len());
         self.apply_add(&mut y, i);
         y
@@ -310,15 +302,12 @@
 }
 
 #[replace_float_literals(F::cast_from(literal))]
-impl<F, B, const N : usize> GEMV<F, DVector<F>>
-for Div<F, B, N>
+impl<F, B, const N: usize> GEMV<F, DVector<F>> for Div<F, B, N>
 where
-    B : Discretisation<F>,
-    F : Float + nalgebra::RealField,
+    B: Discretisation<F>,
+    F: Float + nalgebra::RealField,
 {
-    fn gemv<I : Instance<DVector<F>>>(
-        &self, y : &mut DVector<F>, α : F, i : I, β : F
-    ) {
+    fn gemv<I: Instance<DVector<F>>>(&self, y: &mut DVector<F>, α: F, i: I, β: F) {
         if β == 0.0 {
             y.as_mut_slice().iter_mut().for_each(|x| *x = 0.0);
         } else if β != 1.0 {
@@ -330,26 +319,51 @@
         i.eval(|x| {
             assert_eq!(x.len(), N * m);
             for d in 0..N {
-                let v = x.generic_view((d*m, 0), (Dyn(m), U1));
+                let v = x.generic_view((d * m, 0), (Dyn(m), U1));
                 for b in Iter::new(&self.dims, d) {
-                    self.discretisation.add_diff_mut(y, &v, α/h, b)
+                    self.discretisation.add_diff_mut(y, &v, α / h, b)
                 }
             }
         })
     }
 }
 
-impl<F, B, const N : usize> Grad<F, B, N>
+impl<F, B, const N: usize> Grad<F, B, N>
 where
-    B : Discretisation<F>,
-    F : Float + nalgebra::RealField
+    B: Discretisation<F>,
+    F: Float + nalgebra::RealField,
 {
     /// Creates a new discrete gradient operator for the vector `u`, verifying dimensions.
-    pub fn new_for(u : &DVector<F>, h : F, dims : [usize; N], discretisation : B)
-        -> Option<Self>
-    {
+    pub fn new_for(u: &DVector<F>, h: F, dims: [usize; N], discretisation: B) -> Option<Self> {
         if u.len() == dims.iter().product::<usize>() {
-            Some(Grad { dims, h, discretisation } )
+            Some(Grad {
+                dims,
+                h,
+                discretisation,
+            })
+        } else {
+            None
+        }
+    }
+
+    fn len(&self) -> usize {
+        self.dims.iter().product::<usize>()
+    }
+}
+
+impl<F, B, const N: usize> Div<F, B, N>
+where
+    B: Discretisation<F>,
+    F: Float + nalgebra::RealField,
+{
+    /// Creates a new discrete gradient operator for the vector `u`, verifying dimensions.
+    pub fn new_for(u: &DVector<F>, h: F, dims: [usize; N], discretisation: B) -> Option<Self> {
+        if u.len() == dims.iter().product::<usize>() * N {
+            Some(Div {
+                dims,
+                h,
+                discretisation,
+            })
         } else {
             None
         }
@@ -360,108 +374,84 @@
     }
 }
 
-
-impl<F, B, const N : usize> Div<F, B, N>
+impl<F, B, const N: usize> Linear<DVector<F>> for Grad<F, B, N>
 where
-    B : Discretisation<F>,
-    F : Float + nalgebra::RealField
+    B: Discretisation<F>,
+    F: Float + nalgebra::RealField,
 {
-    /// Creates a new discrete gradient operator for the vector `u`, verifying dimensions.
-    pub fn new_for(u : &DVector<F>, h : F, dims : [usize; N], discretisation : B)
-        -> Option<Self>
-    {
-        if u.len() == dims.iter().product::<usize>() * N {
-            Some(Div { dims, h, discretisation } )
-        } else {
-            None
-        }
-    }
- 
-    fn len(&self) -> usize {
-        self.dims.iter().product::<usize>()
-    }
 }
 
-impl<F, B, const N : usize> Linear<DVector<F>>
-for Grad<F, B, N>
+impl<F, B, const N: usize> Linear<DVector<F>> for Div<F, B, N>
 where
-    B : Discretisation<F>,
-    F : Float + nalgebra::RealField,
+    B: Discretisation<F>,
+    F: Float + nalgebra::RealField,
 {
 }
 
-impl<F, B, const N : usize> Linear<DVector<F>>
-for Div<F, B, N>
+impl<F, B, const N: usize> BoundedLinear<DVector<F>, L2, L2, F> for Grad<F, B, N>
 where
-    B : Discretisation<F>,
-    F : Float + nalgebra::RealField,
+    B: Discretisation<F>,
+    F: Float + nalgebra::RealField,
+    DVector<F>: Norm<F, L2>,
 {
-}
-
-impl<F, B, const N : usize> BoundedLinear<DVector<F>, L2, L2, F>
-for Grad<F, B, N>
-where
-    B : Discretisation<F>,
-    F : Float + nalgebra::RealField,
-    DVector<F> : Norm<F, L2>,
-{
-    fn opnorm_bound(&self, _ : L2, _ : L2) -> F {
+    fn opnorm_bound(&self, _: L2, _: L2) -> DynResult<F> {
         // Fuck nalgebra.
-        self.discretisation.opnorm_bound(num_traits::Float::abs(self.h))
+        self.discretisation
+            .opnorm_bound(num_traits::Float::abs(self.h))
     }
 }
 
-
-impl<F, B, const N : usize> BoundedLinear<DVector<F>, L2, L2, F>
-for Div<F, B, N>
+impl<F, B, const N: usize> BoundedLinear<DVector<F>, L2, L2, F> for Div<F, B, N>
 where
-    B : Discretisation<F>,
-    F : Float + nalgebra::RealField,
-    DVector<F> : Norm<F, L2>,
+    B: Discretisation<F>,
+    F: Float + nalgebra::RealField,
+    DVector<F>: Norm<F, L2>,
 {
-    fn opnorm_bound(&self, _ : L2, _ : L2) -> F {
+    fn opnorm_bound(&self, _: L2, _: L2) -> DynResult<F> {
         // Fuck nalgebra.
-        self.discretisation.opnorm_bound(num_traits::Float::abs(self.h))
+        self.discretisation
+            .opnorm_bound(num_traits::Float::abs(self.h))
     }
 }
 
-impl<F, B, const N : usize>
-Adjointable<DVector<F>, DVector<F>>
-for Grad<F, B, N>
+impl<F, B, const N: usize> Adjointable<DVector<F>, DVector<F>> for Grad<F, B, N>
 where
-    B : Discretisation<F>,
-    F : Float + nalgebra::RealField,
+    B: Discretisation<F>,
+    F: Float + nalgebra::RealField,
 {
     type AdjointCodomain = DVector<F>;
-    type Adjoint<'a> = Div<F, B::Opposite, N> where Self : 'a;
+    type Adjoint<'a>
+        = Div<F, B::Opposite, N>
+    where
+        Self: 'a;
 
     /// Form the adjoint operator of `self`.
     fn adjoint(&self) -> Self::Adjoint<'_> {
         Div {
-            dims : self.dims,
-            h : -self.h,
-            discretisation : self.discretisation.opposite(),
+            dims: self.dims,
+            h: -self.h,
+            discretisation: self.discretisation.opposite(),
         }
     }
 }
 
-
-impl<F, B, const N : usize>
-Adjointable<DVector<F>, DVector<F>>
-for Div<F, B, N>
+impl<F, B, const N: usize> Adjointable<DVector<F>, DVector<F>> for Div<F, B, N>
 where
-    B : Discretisation<F>,
-    F : Float + nalgebra::RealField,
+    B: Discretisation<F>,
+    F: Float + nalgebra::RealField,
 {
     type AdjointCodomain = DVector<F>;
-    type Adjoint<'a> = Grad<F, B::Opposite, N> where Self : 'a;
+    type Adjoint<'a>
+        = Grad<F, B::Opposite, N>
+    where
+        Self: 'a;
 
     /// Form the adjoint operator of `self`.
     fn adjoint(&self) -> Self::Adjoint<'_> {
         Grad {
-            dims : self.dims,
-            h : -self.h,
-            discretisation : self.discretisation.opposite(),
+            dims: self.dims,
+            h: -self.h,
+            discretisation: self.discretisation.opposite(),
         }
     }
 }
@@ -472,8 +462,8 @@
 
     #[test]
     fn grad_adjoint() {
-        let im = DVector::from( (0..9).map(|t| t as f64).collect::<Vec<_>>());
-        let v = DVector::from( (0..18).map(|t| t as f64).collect::<Vec<_>>());
+        let im = DVector::from((0..9).map(|t| t as f64).collect::<Vec<_>>());
+        let v = DVector::from((0..18).map(|t| t as f64).collect::<Vec<_>>());
 
         let grad = Grad::new_for(&im, 1.0, [3, 3], ForwardNeumann).unwrap();
         let a = grad.apply(&im).dot(&v);
@@ -484,6 +474,5 @@
         let a = grad.apply(&im).dot(&v);
         let b = grad.adjoint().apply(&v).dot(&im);
         assert_eq!(a, b);
-
     }
 }

--- a/src/linops.rs	Wed Apr 30 01:06:25 2025 -0500
+++ b/src/linops.rs	Wed Apr 30 16:39:01 2025 -0500
@@ -3,6 +3,7 @@
 */
 
 use crate::direct_product::Pair;
+use crate::error::DynResult;
 use crate::instance::Instance;
 pub use crate::mapping::{Composition, DifferentiableImpl, Mapping, Space};
 use crate::norms::{Linfinity, Norm, NormExponent, PairNorm, L1, L2};
@@ -84,7 +85,9 @@
     /// This is not expected to be the norm, just any bound on it that can be
     /// reasonably implemented. The [`NormExponent`] `xexp`  indicates the norm
     /// in `X`, and `codexp` in the codomain.
-    fn opnorm_bound(&self, xexp: XExp, codexp: CodExp) -> F;
+    ///
+    /// This may fail with an error if the bound is for some reason incalculable.
+    fn opnorm_bound(&self, xexp: XExp, codexp: CodExp) -> DynResult<F>;
 }
 
 // Linear operator application into mutable target. The [`AsRef`] bound
@@ -183,8 +186,8 @@
     F: Num,
     E: NormExponent,
 {
-    fn opnorm_bound(&self, _xexp: E, _codexp: E) -> F {
-        F::ONE
+    fn opnorm_bound(&self, _xexp: E, _codexp: E) -> DynResult<F> {
+        Ok(F::ONE)
     }
 }
 
@@ -267,8 +270,8 @@
     E1: NormExponent,
     E2: NormExponent,
 {
-    fn opnorm_bound(&self, _xexp: E1, _codexp: E2) -> F {
-        F::ZERO
+    fn opnorm_bound(&self, _xexp: E1, _codexp: E2) -> DynResult<F> {
+        Ok(F::ZERO)
     }
 }
 
@@ -351,9 +354,9 @@
     T: BoundedLinear<X, Xexp, Zexp, F, Codomain = Z>,
     S: BoundedLinear<Z, Zexp, Yexp, F>,
 {
-    fn opnorm_bound(&self, xexp: Xexp, yexp: Yexp) -> F {
+    fn opnorm_bound(&self, xexp: Xexp, yexp: Yexp) -> DynResult<F> {
         let zexp = self.intermediate_norm_exponent;
-        self.outer.opnorm_bound(zexp, yexp) * self.inner.opnorm_bound(xexp, zexp)
+        Ok(self.outer.opnorm_bound(zexp, yexp)? * self.inner.opnorm_bound(xexp, zexp)?)
     }
 }
 
@@ -691,12 +694,12 @@
                 &self,
                 PairNorm(expa, expb, _): PairNorm<ExpA, ExpB, $expj>,
                 expr: ExpR,
-            ) -> F {
+            ) -> DynResult<F> {
                 // An application of the triangle inequality bounds the norm by the maximum
                 // of the individual norms. A simple observation shows this to be exact.
-                let na = self.0.opnorm_bound(expa, expr);
-                let nb = self.1.opnorm_bound(expb, expr);
-                na.max(nb)
+                let na = self.0.opnorm_bound(expa, expr)?;
+                let nb = self.1.opnorm_bound(expb, expr)?;
+                Ok(na.max(nb))
             }
         }
 
@@ -715,12 +718,12 @@
                 &self,
                 expa: ExpA,
                 PairNorm(exps, expt, _): PairNorm<ExpS, ExpT, $expj>,
-            ) -> F {
+            ) -> DynResult<F> {
                 // This is based on the rule for RowOp and ‖A^*‖ = ‖A‖, hence,
                 // for A=[S; T], ‖A‖=‖[S^*, T^*]‖ ≤ max{‖S^*‖, ‖T^*‖} = max{‖S‖, ‖T‖}
-                let ns = self.0.opnorm_bound(expa, exps);
-                let nt = self.1.opnorm_bound(expa, expt);
-                ns.max(nt)
+                let ns = self.0.opnorm_bound(expa, exps)?;
+                let nt = self.1.opnorm_bound(expa, expt)?;
+                Ok(ns.max(nt))
             }
         }
     };

--- a/src/mapping.rs	Wed Apr 30 01:06:25 2025 -0500
+++ b/src/mapping.rs	Wed Apr 30 16:39:01 2025 -0500
@@ -2,6 +2,7 @@
 Traits for mathematical functions.
 */
 
+use crate::error::DynResult;
 pub use crate::instance::{BasicDecomposition, Decomposition, Instance, Space};
 use crate::loc::Loc;
 use crate::norms::{Norm, NormExponent};
@@ -324,7 +325,7 @@
     type FloatType: Float;
 
     /// Returns the Lipschitz factor of `self` with respect to the (semi)norm `D`.
-    fn lipschitz_factor(&self, seminorm: M) -> Option<Self::FloatType>;
+    fn lipschitz_factor(&self, seminorm: M) -> DynResult<Self::FloatType>;
 }
 
 /// Helper trait for implementing [`Lipschitz`] for mappings that implement [`DifferentiableImpl`].
@@ -332,7 +333,7 @@
     type FloatType: Float;
 
     /// Compute the lipschitz factor of the derivative of `f`.
-    fn diff_lipschitz_factor(&self, seminorm: M) -> Option<Self::FloatType>;
+    fn diff_lipschitz_factor(&self, seminorm: M) -> DynResult<Self::FloatType>;
 }
 
 impl<'b, M, X, A> Lipschitz<M> for Differential<'b, X, A>
@@ -342,7 +343,7 @@
 {
     type FloatType = A::FloatType;
 
-    fn lipschitz_factor(&self, seminorm: M) -> Option<Self::FloatType> {
+    fn lipschitz_factor(&self, seminorm: M) -> DynResult<Self::FloatType> {
         (*self.g).diff_lipschitz_factor(seminorm)
     }
 }

--- a/src/mapping/dataterm.rs	Wed Apr 30 01:06:25 2025 -0500
+++ b/src/mapping/dataterm.rs	Wed Apr 30 16:39:01 2025 -0500
@@ -7,11 +7,13 @@
 
 use super::{DifferentiableImpl, DifferentiableMapping, LipschitzDifferentiableImpl, Mapping};
 use crate::convex::ConvexMapping;
+use crate::error::DynResult;
 use crate::instance::{Instance, Space};
 use crate::linops::{BoundedLinear, Linear, Preadjointable};
 use crate::norms::{Normed, L2};
 use crate::types::Float;
 use std::ops::Sub;
+
 //use serde::{Deserialize, Serialize};
 
 /// Functions of the form $\frac{1}{2}\|Ax-b\|_2^2$ for an operator $A$
@@ -114,7 +116,7 @@
 {
     type FloatType = F;
 
-    fn diff_lipschitz_factor(&self, seminorm: X::NormExp) -> Option<F> {
-        Some(self.opA.opnorm_bound(seminorm, L2).powi(2))
+    fn diff_lipschitz_factor(&self, seminorm: X::NormExp) -> DynResult<F> {
+        Ok(self.opA.opnorm_bound(seminorm, L2)?.powi(2))
     }
 }

--- a/src/mapping/quadratic.rs	Wed Apr 30 01:06:25 2025 -0500
+++ b/src/mapping/quadratic.rs	Wed Apr 30 16:39:01 2025 -0500
@@ -7,6 +7,7 @@
 
 use super::{DifferentiableImpl, LipschitzDifferentiableImpl, Mapping};
 use crate::convex::ConvexMapping;
+use crate::error::DynResult;
 use crate::euclidean::Euclidean;
 use crate::instance::{Instance, Space};
 use crate::linops::{BoundedLinear, Linear, Preadjointable};
@@ -93,7 +94,7 @@
 {
     type FloatType = F;
 
-    fn diff_lipschitz_factor(&self, seminorm: ExpX) -> Option<F> {
-        Some(self.opA.opnorm_bound(seminorm, L2).powi(2))
+    fn diff_lipschitz_factor(&self, seminorm: ExpX) -> DynResult<F> {
+        Ok(self.opA.opnorm_bound(seminorm, L2)?.powi(2))
     }
 }