RLSE: Ver 0.30.11

* Cholesky Decomposition (#46)
* Add `is_symmetric`

Author: Axect

Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "peroxide"
-version = "0.30.10"
+version = "0.30.11"
 authors = ["axect <[email protected]>"]
 edition = "2018"
 description = "Rust comprehensive scientific computation library contains linear algebra, numerical analysis, statistics and machine learning tools with farmiliar syntax"

README.md

@@ -128,6 +128,7 @@ Peroxide can do many things.
     * LU Decomposition, Inverse matrix, Block partitioning
     * QR Decomposition (`O3` feature)
     * Singular Value Decomposition (SVD) (`O3` feature)
+    * Cholesky Decomposition (`O3` feature)
     * Reduced Row Echelon form
     * Column, Row operations
     * Eigenvalue, Eigenvector
@@ -210,7 +211,7 @@ then Rust become great choice.
 
 ## Latest README version
 
-Corresponding to `0.30.2`
+Corresponding to `0.30.11`
 
 ## Pre-requisite
 
@@ -265,7 +266,7 @@ Corresponding to `0.30.2`
 
 ## Useful tips for features
 
-* If you want to use `QR` or `SVD` then should use `O3` feature (there are no implementations for these decompositions in `default`)
+* If you want to use *QR* or *SVD* or *Cholesky Decomposition* then should use `O3` feature (there are no implementations for these decompositions in `default`)
 * If you want to write your numerical results, then use `nc` feature and `netcdf` format. (It is much more effective than `csv` and `json`.)
 * To plot, use `nc` feature to export data as netcdf format and use python to draw plot.
     * `plot` feature has limited plot abilities.

RELEASES.md

@@ -1,3 +1,8 @@
+# Release 0.30.11 (2022-02-10)
+
+* Implement Cholesky decomposition in `O3` feature.
+* Implement symmetricity check method - `is_symmetric` for Matrix. 
+
 # Release 0.30.10 (2022-02-05)
 
 * Update `netcdf` dependency to `0.7`

src/lib.rs

@@ -7,9 +7,10 @@
 //! * Linear Algebra (with BLAS & LAPACK)
 //!     * [Matrix](structure/matrix/index.html) operations
 //!         * `+,-,*,/`
-//!         * LU, Determinant, Inverse
+//!         * LU Decomposition, Determinant, Inverse
 //!         * QR Decomposition (`O3` feature needed)
 //!         * Singular Value Decomposition (`O3` feature needed)
+//!         * Cholesky Decomposition (`O3` feature needed)
 //!         * Reduced Row Echelon Form
 //!     * [Vector](structure/vector/index.html) operations
 //!     * [Eigenvalue, Eigenvector](numerical/eigen/index.html) algorithms
@@ -80,7 +81,7 @@
 //! ### Cargo.toml
 //!
 //! * To use `peroxide`, you should edit `Cargo.toml`
-//! * Current document version is corresponding to `0.30.2`
+//! * Current document version is corresponding to `0.30.11`
 //!
 //! 1. Default
 //!     ```toml
@@ -159,7 +160,7 @@
 //! ## Useful tips for features
 //!
 //! * After `0.28.0`, `dataframe` feature is replaced by `nc` feature.
-//! * If you want to use `QR` or `SVD` then should use `O3` feature (there are no implementations for these decompositions in `default`)
+//! * If you want to use *QR* or *SVD* or *Cholesky Decomposition* then should use `O3` feature (there are no implementations for these decompositions in `default`)
 //! * If you want to write your numerical results, then use `nc` feature and `netcdf` format. (It is much more effective than `csv` and `json`.)
 //! * After `0.23.0`, there are two options - `fuga`, `prelude`. Choose proper option for your computations.
 //! * To plot, use `nc` feature to export data as netcdf format and use python to draw plot.

src/structure/matrix.rs

@@ -561,6 +561,30 @@
 //!     }
 //!     ```
 //!
+//! ## Cholesky Decomposition
+//!
+//! * Use `dpotrf` of LAPACK
+//! * Return Matrix (But there can be panic! - Not symmetric or Not positive definite)
+//! * Example
+//!
+//!     ```rust
+//!     extern crate peroxide;
+//!     use peroxide::fuga::*;
+//!
+//!     fn main() {
+//!         let a = ml_matrix("1 2;2 5");
+//!         #[cfg(feature = "O3")]
+//!         {
+//!             let u = a.cholesky(Upper);
+//!             assert_eq!(u, ml_matrix("1 2;0 1"));
+//!
+//!             let l = a.cholesky(Lower);
+//!             assert_eq!(l, ml_matrix("1 0;2 1"));
+//!         }
+//!         a.print();
+//!     }
+//!     ```
+//!
 //! ## Moore-Penrose Pseudo Inverse
 //!
 //! * $ X^\dagger = \left(X^T X\right)^{-1} X^T $