Fix some typos

Signed-off-by: Alexander Seiler <[email protected]>

README.md

@@ -47,7 +47,7 @@ should be solved when **_b_** is not in the range of **_A_** (inconsistent syste
   * **_A_** is square and singular,
   * **_A_** is tall and thin.
 
-Underdetermined sytems are less common but also occur.
+Underdetermined systems are less common but also occur.
 
 If there are infinitely many such **_x_** (because **_A_** is column rank-deficient), one with minimum norm is identified
 
@@ -61,12 +61,12 @@ If there are infinitely many such **_x_** (because **_A_** is column rank-defici
   minimize ‖<b><i>x</i></b>‖ &nbsp; subject to &nbsp; <b><i>Ax = b</i></b>
 </p>
 
-sould be solved when **_A_** is column rank-deficient but **_b_** is in the range of **_A_** (consistent systems), regardless of the shape of **_A_**.
+should be solved when **_A_** is column rank-deficient but **_b_** is in the range of **_A_** (consistent systems), regardless of the shape of **_A_**.
 This situation mainly occurs when
   * **_A_** is square and singular,
   * **_A_** is short and wide.
 
-Overdetermined sytems are less common but also occur.
+Overdetermined systems are less common but also occur.
 
 4. Adjoint systems
 

docs/src/factorization-free.md

@@ -59,9 +59,9 @@ where
 * `type` is the operator element type;
 * `nrow` and `ncol` are its dimensions;
 * `symmetric` and `hermitian` should be set to `true` or `false`;
-* `prod(y, v)`, `tprod(y, w)` and `ctprod(u, w)` are called when writing `mul!(y, A, v)`, `mul!(y, tranpose(A), w)`, and `mul!(y, A', u)`, respectively.
+* `prod(y, v)`, `tprod(y, w)` and `ctprod(u, w)` are called when writing `mul!(y, A, v)`, `mul!(y, transpose(A), w)`, and `mul!(y, A', u)`, respectively.
 
-See the [tutorial](https://juliasmoothoptimizers.github.io/tutorials/introduction-to-linear-operators/) and the detailed [documentation](https://juliasmoothoptimizers.github.io/LinearOperators.jl/dev/) for more informations on `LinearOperators.jl`.
+See the [tutorial](https://juliasmoothoptimizers.github.io/tutorials/introduction-to-linear-operators/) and the detailed [documentation](https://juliasmoothoptimizers.github.io/LinearOperators.jl/dev/) for more information on `LinearOperators.jl`.
 
 ## Examples
 

docs/src/index.md

@@ -22,7 +22,7 @@ should be solved when **_b_** is not in the range of **_A_** (inconsistent syste
   * **_A_** is square and singular,
   * **_A_** is tall and thin.
 
-Underdetermined sytems are less common but also occur.
+Underdetermined systems are less common but also occur.
 
 If there are infinitely many such **_x_** (because **_A_** is column rank-deficient), one with minimum norm is identified
 
@@ -36,12 +36,12 @@ If there are infinitely many such **_x_** (because **_A_** is column rank-defici
   \min \|x\| \quad \text{subject to} \quad Ax = b
 ```
 
-sould be solved when **_A_** is column rank-deficient but **_b_** is in the range of **_A_** (consistent systems), regardless of the shape of **_A_**.
+should be solved when **_A_** is column rank-deficient but **_b_** is in the range of **_A_** (consistent systems), regardless of the shape of **_A_**.
 This situation mainly occurs when
   * **_A_** is square and singular,
   * **_A_** is short and wide.
 
-Overdetermined sytems are less common but also occur.
+Overdetermined systems are less common but also occur.
 
 4 - Adjoint systems
 

docs/src/tips.md

@@ -16,7 +16,7 @@ If you don't know the maximum number of threads available on your computer, you
 NMAX = Sys.CPU_THREADS
 ```
 
-and define the number of OpenBLAS/MKL threads at runtine with
+and define the number of OpenBLAS/MKL threads at runtime with
 
 ```julia
 BLAS.set_num_threads(N)  # 1 ≤ N ≤ NMAX

docs/src/warm-start.md

@@ -58,8 +58,8 @@ y = y₀ + Δy
 ```@meta
 # ## Restarted methods
 #
-# The storage requierements of Krylov methods based on the Arnoldi process, such as FOM and GMRES, increase as the iteration progresses.
-# For very large problems, the storage costs become prohibitive after only few iterations and restarted variants FOM(k) and GMRES(k) are prefered.
+# The storage requirements of Krylov methods based on the Arnoldi process, such as FOM and GMRES, increase as the iteration progresses.
+# For very large problems, the storage costs become prohibitive after only few iterations and restarted variants FOM(k) and GMRES(k) are preferred.
 # In this section, we show how to use warm starts to implement GMRES(k) and FOM(k).
 #
 # ```julia