Change the category of USYMLQ and USYMQR

docs/src/solvers/ln.md

@@ -36,3 +36,10 @@ craig!
 craigmr
 craigmr!
 ```
+
+## USYMLQ
+
+```@docs
+usymlq
+usymlq!
+```

docs/src/solvers/ls.md

@@ -36,3 +36,10 @@ lsqr!
 lsmr
 lsmr!
 ```
+
+## USYMQR
+
+```@docs
+usymqr
+usymqr!
+```

docs/src/solvers/unsymmetric.md

@@ -16,20 +16,6 @@ qmr
 qmr!
 ```
 
-## USYMLQ
-
-```@docs
-usymlq
-usymlq!
-```
-
-## USYMQR
-
-```@docs
-usymqr
-usymqr!
-```
-
 ## CGS
 
 ```@docs

src/usymlq.jl

@@ -32,7 +32,7 @@ export usymlq, usymlq!
 
 USYMLQ can be warm-started from an initial guess `x0` where `kwargs` are the same keyword arguments as above.
 
-Solve the linear system Ax = b of size m × n using the USYMLQ method.
+USYMLQ determines the least-norm solution of the consistent linear system Ax = b of size m × n.
 
 USYMLQ is based on the orthogonal tridiagonalization process and requires two initial nonzero vectors `b` and `c`.
 The vector `c` is only used to initialize the process and a default value can be `b` or `Aᴴb` depending on the shape of `A`.

src/usymqr.jl

@@ -31,7 +31,8 @@ export usymqr, usymqr!
 
 USYMQR can be warm-started from an initial guess `x0` where `kwargs` are the same keyword arguments as above.
 
-Solve the linear system Ax = b of size m × n using USYMQR.
+USYMQR solves the linear least-squares problem min ‖b - Ax‖² of size m × n.
+If A is square and nonsingular, USYMQR solves Ax = b.
 
 USYMQR is based on the orthogonal tridiagonalization process and requires two initial nonzero vectors `b` and `c`.
 The vector `c` is only used to initialize the process and a default value can be `b` or `Aᴴb` depending on the shape of `A`.