Merge branch 'lobpcg-fix' of github.com:bytesnake/ndarray-linalg into lobpcg-fix

Author: bytesnake

.github/workflows/intel-mkl.yml

@@ -34,7 +34,7 @@ jobs:
 
   linux-container:
     runs-on: ubuntu-18.04
-    container: rustmath/mkl-rust:1.43.0
+    container: ghcr.io/rust-math/intel-mkl-src/mkl-rust:1.49.0
     steps:
       - uses: actions/checkout@v1
       - uses: actions-rs/cargo@v1

.github/workflows/rust.yml

@@ -27,7 +27,7 @@ jobs:
   coverage:
     runs-on: ubuntu-18.04
     container:
-      image: rustmath/mkl-rust:1.43.0
+      image: ghcr.io/rust-math/intel-mkl-src/mkl-rust:1.49.0
       options: --security-opt seccomp=unconfined
     steps:
       - uses: actions/checkout@v2

CHANGELOG.md

@@ -1,6 +1,11 @@
 Unreleased
 -----------
 
+Updated dependencies
+---------------------
+- ndarray 0.15 https://github.com/rust-ndarray/ndarray-linalg/pull/273
+- cauchy 0.4 (num-complex 0.4, rand 0.8), lapack 0.18 https://github.com/rust-ndarray/ndarray-linalg/pull/276
+
 0.13.1 - 13 March 2021
 =====================
 

lax/Cargo.toml

@@ -29,10 +29,10 @@ intel-mkl-static = ["intel-mkl-src/mkl-static-lp64-seq", "intel-mkl-src/download
 intel-mkl-system = ["intel-mkl-src/mkl-dynamic-lp64-seq"]
 
 [dependencies]
-thiserror = "1.0.23"
-cauchy = "0.3.0"
+thiserror = "1.0.24"
+cauchy = "0.4.0"
 num-traits = "0.2.14"
-lapack = "0.17.0"
+lapack = "0.18.0"
 
 [dependencies.intel-mkl-src]
 version = "0.6.0"
@@ -46,7 +46,7 @@ features = ["cblas"]
 default-features = false
 
 [dependencies.openblas-src]
-version = "0.10.2"
+version = "0.10.4"
 optional = true
 default-features = false
 features = ["cblas"]

ndarray-linalg/Cargo.toml

@@ -29,15 +29,15 @@ intel-mkl-static = ["lax/intel-mkl-static"]
 intel-mkl-system = ["lax/intel-mkl-system"]
 
 [dependencies]
-cauchy = "0.3.0"
-num-complex = "0.3.1"
-num-traits  = "0.2.11"
-rand = "0.7.3"
-thiserror = "1.0.20"
+cauchy = "0.4.0"
+num-complex = "0.4.0"
+num-traits  = "0.2.14"
+rand = "0.8.3"
+thiserror = "1.0.24"
 
 [dependencies.ndarray]
-version = "0.14"
-features = ["blas", "approx"]
+version = "0.15.1"
+features = ["blas", "approx", "std"]
 default-features = false
 
 [dependencies.lax]
@@ -46,8 +46,8 @@ path = "../lax"
 default-features = false
 
 [dev-dependencies]
-paste = "1.0"
-criterion = "0.3"
+paste = "1.0.5"
+criterion = "0.3.4"
 # Keep the same version as ndarray's dependency!
 approx = { version = "0.4", features = ["num-complex"] }
 rand_xoshiro = "0.4"

ndarray-linalg/src/cholesky.rs

@@ -26,7 +26,7 @@
 //!
 //! // Obtain `L`
 //! let lower = a.cholesky(UPLO::Lower).unwrap();
-//! assert!(lower.all_close(&array![
+//! assert!(lower.abs_diff_eq(&array![
 //!     [ 2., 0., 0.],
 //!     [ 6., 1., 0.],
 //!     [-8., 5., 3.]
@@ -39,7 +39,7 @@
 //! // Solve `A * x = b`
 //! let b = array![4., 13., -11.];
 //! let x = a.solvec(&b).unwrap();
-//! assert!(x.all_close(&array![-2., 1., 0.], 1e-9));
+//! assert!(x.abs_diff_eq(&array![-2., 1., 0.], 1e-9));
 //! # }
 //! ```
 

ndarray-linalg/src/eigh.rs

@@ -8,7 +8,6 @@ use crate::layout::*;
 use crate::operator::LinearOperator;
 use crate::types::*;
 use crate::UPLO;
-use std::iter::FromIterator;
 
 /// Eigenvalue decomposition of Hermite matrix reference
 pub trait Eigh {

ndarray-linalg/src/lobpcg/eig.rs

@@ -222,9 +222,9 @@ impl<A: Float + Scalar + ScalarOperand + Lapack + PartialOrd + Default> Iterator
                 // add the new eigenvector to the internal constrain matrix
                 let new_constraints = if let Some(ref constraints) = self.eig.constraints {
                     let eigvecs_arr: Vec<_> = constraints
-                        .gencolumns()
+                        .columns()
                         .into_iter()
-                        .chain(vecs.gencolumns().into_iter())
+                        .chain(vecs.columns().into_iter())
                         .collect();
 
                     stack(Axis(1), &eigvecs_arr).unwrap()

ndarray-linalg/src/lobpcg/lobpcg.rs

@@ -81,7 +81,7 @@ fn apply_constraints<A: Scalar + Lapack>(
     let gram_yv = y.t().dot(&v);
 
     let u = gram_yv
-        .gencolumns()
+        .columns()
         .into_iter()
         .map(|x| {
             let res = cholesky_yy.solvec(&x).unwrap();
@@ -222,7 +222,7 @@ pub fn lobpcg<
 
         // calculate L2 norm of error for every eigenvalue
         let residual_norms = r
-            .gencolumns()
+            .columns()
             .into_iter()
             .map(|x| x.norm())
             .collect::<Vec<A::Real>>();

ndarray-linalg/src/lobpcg/svd.rs

@@ -64,7 +64,7 @@ impl<A: Float + PartialOrd + DivAssign<A> + 'static + MagnitudeCorrection> Trunc
             let mut ularge = self.problem.dot(&vlarge);
 
             ularge
-                .gencolumns_mut()
+                .columns_mut()
                 .into_iter()
                 .zip(values.iter())
                 .for_each(|(mut a, b)| a.mapv_inplace(|x| x / *b));
@@ -75,7 +75,7 @@ impl<A: Float + PartialOrd + DivAssign<A> + 'static + MagnitudeCorrection> Trunc
 
             let mut vlarge = self.problem.t().dot(&ularge);
             vlarge
-                .gencolumns_mut()
+                .columns_mut()
                 .into_iter()
                 .zip(values.iter())
                 .for_each(|(mut a, b)| a.mapv_inplace(|x| x / *b));

ndarray-linalg/src/solve.rs

@@ -16,7 +16,7 @@
 //! let a: Array2<f64> = array![[3., 2., -1.], [2., -2., 4.], [-2., 1., -2.]];
 //! let b: Array1<f64> = array![1., -2., 0.];
 //! let x = a.solve_into(b).unwrap();
-//! assert!(x.all_close(&array![1., -2., -2.], 1e-9));
+//! assert!(x.abs_diff_eq(&array![1., -2., -2.], 1e-9));
 //!
 //! # }
 //! ```

ndarray-linalg/src/solveh.rs

@@ -23,7 +23,7 @@
 //! ];
 //! let b: Array1<f64> = array![11., -12., 1.];
 //! let x = a.solveh_into(b).unwrap();
-//! assert!(x.all_close(&array![1., 3., -2.], 1e-9));
+//! assert!(x.abs_diff_eq(&array![1., 3., -2.], 1e-9));
 //!
 //! # }
 //! ```

ndarray-linalg/tests/cholesky.rs

@@ -140,7 +140,7 @@ macro_rules! cholesky_det {
                     .eigvalsh(UPLO::Upper)
                     .unwrap()
                     .mapv(|elem| elem.ln())
-                    .scalar_sum();
+                    .sum();
                 let det = ln_det.exp();
                 assert_aclose!(a.factorizec(UPLO::Upper).unwrap().detc(), det, $atol);
                 assert_aclose!(a.factorizec(UPLO::Upper).unwrap().ln_detc(), ln_det, $atol);