From 130bcae4f36d333fdd3cc04bd4831d3876e48557 Mon Sep 17 00:00:00 2001 From: Toshiki Teramura Date: Fri, 30 Sep 2022 01:31:28 +0900 Subject: [PATCH] Update doc-tests --- lax/src/lib.rs | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/lax/src/lib.rs b/lax/src/lib.rs index 6ed8498c..061aa737 100644 --- a/lax/src/lib.rs +++ b/lax/src/lib.rs @@ -7,15 +7,15 @@ //! For example, LU decomposition to double-precision matrix is provided like: //! //! ```ignore -//! impl Solve_ for f64 { +//! impl Lapack for f64 { //! fn lu(l: MatrixLayout, a: &mut [Self]) -> Result { ... } //! } //! ``` //! -//! see [Solve_] for detail. You can use it like `f64::lu`: +//! see [Lapack] for detail. You can use it like `f64::lu`: //! //! ``` -//! use lax::{Solve_, layout::MatrixLayout, Transpose}; +//! use lax::{Lapack, layout::MatrixLayout, Transpose}; //! //! let mut a = vec![ //! 1.0, 2.0, @@ -31,9 +31,9 @@ //! this trait can be used as a trait bound: //! //! ``` -//! use lax::{Solve_, layout::MatrixLayout, Transpose}; +//! use lax::{Lapack, layout::MatrixLayout, Transpose}; //! -//! fn solve_at_once(layout: MatrixLayout, a: &mut [T], b: &mut [T]) -> Result<(), lax::error::Error> { +//! fn solve_at_once(layout: MatrixLayout, a: &mut [T], b: &mut [T]) -> Result<(), lax::error::Error> { //! let pivot = T::lu(layout, a)?; //! T::solve(layout, Transpose::No, a, &pivot, b)?; //! Ok(()) @@ -48,7 +48,7 @@ //! //! According to the property input metrix, several types of triangular decomposition are used: //! -//! - [Solve_] trait provides methods for LU-decomposition for general matrix. +//! - [solve] module provides methods for LU-decomposition for general matrix. //! - [Solveh_] triat provides methods for Bunch-Kaufman diagonal pivoting method for symmetric/hermite indefinite matrix. //! - [Cholesky_] triat provides methods for Cholesky decomposition for symmetric/hermite positive dinite matrix. //!