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<Pivot> { ... }
 //! }
 //! ```
 //!
-//! 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<T: Solve_>(layout: MatrixLayout, a: &mut [T], b: &mut [T]) -> Result<(), lax::error::Error> {
+//! fn solve_at_once<T: Lapack>(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.
 //!