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s/factorization/decomposition/g
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lax/src/lib.rs

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@@ -4,7 +4,7 @@
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//! -------------------------------
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//!
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//! This crates provides LAPACK wrapper as `impl` of traits to base scalar types.
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//! For example, LU factorization to double-precision matrix is provided like:
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//! For example, LU decomposition to double-precision matrix is provided like:
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//!
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//! ```ignore
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//! impl Solve_ for f64 {
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//! Linear equation, Inverse matrix, Condition number
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//! --------------------------------------------------
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//!
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//! According to the property input metrix, several types of triangular factorization are used:
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//! According to the property input metrix, several types of triangular decomposition are used:
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//!
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//! - [Solve_] trait provides methods for LU-decomposition for general matrix.
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//! - [Solveh_] triat provides methods for Bunch-Kaufman diagonal pivoting method for symmetric/hermite indefinite matrix.

lax/src/solve.rs

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@@ -5,7 +5,7 @@ use num_traits::{ToPrimitive, Zero};
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#[cfg_attr(doc, katexit::katexit)]
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/// Solve linear equations using LU-decomposition
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///
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/// For a given matrix $A$, LU decomposition is described as $PA = LU$ where
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/// For a given matrix $A$, LU decomposition is described as $A = PLU$ where:
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///
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/// - $L$ is lower matrix
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/// - $U$ is upper matrix
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///
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/// 1. Factorize input matrix $A$ into $L$, $U$, and $P$.
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/// 2. Solve linear equation $Ax = b$ or compute inverse matrix $A^{-1}$
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/// using the output of LU factorization.
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/// using the output of LU decomposition.
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///
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pub trait Solve_: Scalar + Sized {
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/// Computes the LU factorization of a general $m \times n$ matrix
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/// Computes the LU decomposition of a general $m \times n$ matrix
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/// with partial pivoting with row interchanges.
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///
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/// Output
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/// -------
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/// - $U$ and $L$ are stored in `a` after LU factorization has succeeded.
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/// - $U$ and $L$ are stored in `a` after LU decomposition has succeeded.
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/// - $P$ is returned as [Pivot]
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///
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/// Error

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