numpy
diff --git a/‎numpy/core/src/umath/gufuncs_linalg.py
Lines changed: 35 additions & 1 deletion b/‎numpy/core/src/umath/gufuncs_linalg.py
Lines changed: 35 additions & 1 deletion
@@ -13,6 +13,11 @@
 appropriate kernel depending on the parameters. All wrappers forward the keyword
 parameters to the underlying generalized ufunc (the kernel).
 
+The functions are intended to be used on arrays of functions. For those
+functions where a result may not be possible to obtain (like the inverse of
+a matrix that is not invertible) no exception is raised, but the results for
+the elements involved are set to NaN.
+
 Additional functions some fused arithmetic, useful for efficient operation over
 """
 
@@ -283,7 +288,7 @@ def inv(a, **kwargs):
     Implemented for types single, double, csingle and cdouble. Numpy conversion
     rules apply.
 
-    Singular matrices and thus, not invertible, result in an array of NANs.
+    Singular matrices and thus, not invertible, result in an array of NaNs.
 
     See Also
     --------
@@ -328,6 +333,7 @@ def cholesky(a, UPLO='L', **kwargs):
     See Also
     --------
     chosolve : solve a system using cholesky decomposition
+    poinv : compute the inverse of a matrix using cholesky decomposition
 
     Notes
     -----
@@ -336,6 +342,10 @@ def cholesky(a, UPLO='L', **kwargs):
     Implemented for types single, double, csingle and cdouble. Numpy conversion
     rules apply.
 
+    Decomposition is performed using LAPACK routine _potrf.
+
+    For elements where the LAPACK routine fails, the result will be set to NaNs.
+
     If an element of the source array is not a positive-definite matrix the
     result for that element is undefined.
 
@@ -404,6 +414,9 @@ def eig(a, **kwargs):
     This is implemented using the _geev LAPACK routines which compute
     the eigenvalues and eigenvectors of general square arrays.
 
+    For elements where the LAPACK routine fails, the result will be set
+    to NaNs.
+
     Examples
     --------
 
@@ -481,6 +494,9 @@ def eigvals(a, **kwargs):
     This is implemented using the _geev LAPACK routines which compute
     the eigenvalues and eigenvectors of general square arrays.
 
+    For elements where the LAPACK routine fails, the result will be set
+    to NaNs.
+
     Examples
     --------
 
@@ -846,6 +862,9 @@ def eigh(A, UPLO='L', **kw_args):
     The eigenvalues/eigenvectors are computed using LAPACK routines _ssyevd,
     _heevd
 
+    For elements where the LAPACK routine fails, the result will be set
+    to NaNs.
+
     Implemented for single, double, csingle and cdouble. Numpy conversion
     rules apply.
 
@@ -893,6 +912,9 @@ def eigvalsh(A, UPLO='L', **kw_args):
 
     The eigenvalues are computed using LAPACK routines _ssyevd, _heevd
 
+    For elements where the LAPACK routine fails, the result will be set
+    to NaNs.
+
     Implemented for single, double, csingle and cdouble. Numpy conversion
     rules apply.
 
@@ -939,6 +961,9 @@ def solve(A,B,**kw_args):
 
     The solutions are computed using LAPACK routine _gesv
 
+    For elements where the LAPACK routine fails, the result will be set
+    to NaNs.
+
     Implemented for single, double, csingle and cdouble. Numpy conversion
     rules apply.
 
@@ -996,6 +1021,9 @@ def svd(a, full_matrices=1, compute_uv=1 ,**kw_args):
 
     Singular Value Decomposition is performed using LAPACK routine _gesdd
 
+    For elements where the LAPACK routine fails, the result will be set
+    to NaNs.
+
     Implemented for types single, double, csingle and cdouble. Numpy conversion
     rules apply.
 
@@ -1055,6 +1083,9 @@ def chosolve(A, B, UPLO='L', **kw_args):
 
     The solutions are computed using LAPACK routines _potrf, _potrs
 
+    For elements where the LAPACK routine fails, the result will be set
+    to NaNs.
+
     Implemented for single, double, csingle and cdouble. Numpy conversion
     rules apply.
 
@@ -1106,6 +1137,9 @@ def poinv(A, UPLO='L', **kw_args):
 
     The inverse is computed using LAPACK routines _potrf, _potri
 
+    For elements where the LAPACK routine fails, the result will be set
+    to NaNs.
+
     Implemented for types single, double, csingle and cdouble. Numpy conversion
     rules apply.