--- a/scripts/sparse/private/__sprand_impl__.m	Sat Mar 22 13:23:41 2014 +0100
+++ b/scripts/sparse/private/__sprand_impl__.m	Sun Mar 23 20:35:22 2014 -0700
@@ -22,8 +22,8 @@
 
 ## -*- texinfo -*-
 ## @deftypefn  {Function File} {} __sprand_impl__ (@var{s}, @var{randfun})
-## @deftypefnx {Function File} {} __sprand_impl__ (@var{m}, @var{n}, @var{d}, @var{funname}, @var{randfun})
-## @deftypefnx {Function File} {} __sprand_impl__ (@var{m}, @var{n}, @var{d}, @var{rc}, @var{funname}, @var{randfun})
+## @deftypefnx {Function File} {} __sprand_impl__ (@var{m}, @var{n}, @var{d}, @var{fcnname}, @var{randfun})
+## @deftypefnx {Function File} {} __sprand_impl__ (@var{m}, @var{n}, @var{d}, @var{rc}, @var{fcnname}, @var{randfun})
 ## Undocumented internal function.
 ## @end deftypefn
 
@@ -32,46 +32,40 @@
 function S = __sprand_impl__ (varargin)
 
   if (nargin == 2)
-    m = varargin{1};
-    randfun = varargin{2};
+    [m, randfun] = deal (varargin{1:2});
     [i, j] = find (m);
     [nr, nc] = size (m);
     S = sparse (i, j, randfun (size (i)), nr, nc);
-    return;
   else
     if (nargin == 5)
-      [m, n, d, funname, randfun] = deal(varargin{:});
-    else 
-      [m, n, d, rc, funname, randfun] = deal(varargin{:});
+      [m, n, d, fcnname, randfun] = deal (varargin{:});
+    else
+      [m, n, d, rc, fcnname, randfun] = deal (varargin{:});
     endif
 
-    if (!(isscalar (m) && m == fix (m) && m > 0))
-      error ("%s: M must be an integer greater than 0", funname);
+    if (! (isscalar (m) && m == fix (m) && m > 0))
+      error ("%s: M must be an integer greater than 0", fcnname);
     endif
-
-    if (!(isscalar (n) && n == fix (n) && n > 0))
-      error ("%s: N must be an integer greater than 0", funname);
+    if (! (isscalar (n) && n == fix (n) && n > 0))
+      error ("%s: N must be an integer greater than 0", fcnname);
     endif
-
     if (d < 0 || d > 1)
-      error ("%s: density D must be between 0 and 1", funname);
+      error ("%s: density D must be between 0 and 1", fcnname);
     endif
 
-
     if (nargin == 5)
       mn = m*n;
       k = round (d*mn);
       if (mn > sizemax ())
         ## randperm will overflow, so use alternative methods
 
-        idx = unique (fix (rand (min (k*1.01, k+10), 1) * mn)) + 1;
+        idx = unique (fix (rand (1.01*k, 1) * mn)) + 1;
 
         ## idx contains random numbers in [1,mn]
-        ## generate 1% or 10 more random values than necessary in order to
-        ## reduce the probability that there are less than k distinct
-        ## values; maybe a better strategy could be used but I don't think
-        ## it's worth the price
-        
+        ## Generate 1% more random values than necessary in order to reduce the
+        ## probability that there are less than k distinct values; maybe a
+        ## better strategy could be used but I don't think it's worth the price.
+
         ## actual number of entries in S
         k = min (length (idx), k);
         j = floor ((idx(1:k) - 1) / m);
@@ -83,59 +77,69 @@
       endif
 
       S = sparse (i, j, randfun (k, 1), m, n);
+
     elseif (nargin == 6)
-      ## We assume that we want to reverse A=U*S*V' so firstly S is constructed
-      ## and then U = U1*U2*..Un and V' = V1*V2*..Vn  are seen as Jacobi rotation matrices with angles and
-      ## planes of rotation randomized. In the nth step the density required for A is achieved.
+      ## Create a matrix with specified reciprocal condition number.
+
+      if (! isscalar (rc) && ! isvector (rc))
+        error ("%s: RC must be a scalar or vector", fcnname);
+      endif
+
+      ## We want to reverse singular valued decomposition A=U*S*V'.
+      ## First, first S is constructed and then U = U1*U2*..Un and
+      ## V' = V1*V2*..Vn are seen as Jacobi rotation matrices with angles and
+      ## planes of rotation randomized.  Repeatedly apply rotations until the
+      ## required density for A is achieved.
 
-      mynnz = round (m * n * d);
-      if (!isscalar(rc)) 
-        ## Only the min(m, n) greater singular values from rc vector are used. Needed to be compliant.
+      if (isscalar (rc))
+        if (rc < 0 || rc > 1)
+          error ("%s: reciprocal condition number RC must be between 0 and 1", fcnname);
+        endif
+        ## Reciprocal condition number is ratio of smallest SV to largest SV
+        ## Generate singular values randomly and sort them to build S
+        ## Random singular values in range [rc, 1].
+        v = rand (1, min (m,n)) * (1 - rc) + rc;
+        v(1) = 1;
+        v(end) = rc;
+        v = sort (v, "descend");
+        S = sparse (diag (v, m, n));
+      else
+        ## Only the min (m, n) greater singular values from rc vector are used.
         if (length (rc) > min (m,n))
           rc = rc(1:min(m, n));
         endif
-        S = sparse (diag (sort (rc, 'descend'), m, n));
-      else
-        if(rc < 0 || rc > 1)
-          error ("%s: reciprocal condition number rc must be between 0 and 1", funname);
-        endif
-        ## Generate the singular values randomly and sort them to build S
-        for (i = 1:min(m, n))
-          ## Randon singular values between 1 and rc.
-          v(i) = rand () * (1 - rc) + rc; 
-        endfor
-        v(1) = 1;
-        v(end) = rc;
-        v = sort (v, 'descend');
-        S = sparse (diag (v, m, n));
+        S = sparse (diag (sort (rc, "descend"), m, n));
       endif
-      while (nnz(S) < mynnz) 
-        [mm, nn] = size(S);
-        rot_angleu = 2 * randfun () * pi;
-        rot_anglev = 2 * randfun () * pi;
-        cu = cos (rot_angleu); cv = cos (rot_anglev); 
-        su = sin (rot_angleu); sv = sin (rot_anglev);
-        ## Rotation related with U
-        i = fix (rand () * m) + 1;
-        do
-          ## If j==i rotation matrix would be no longer that kind
-          j = fix (rand () * m) + 1;   
-        until (j != i)
-        U = sparse (eye (m,m));
-        U(i, i) = cu; U(i, j) = -su;
-        U(j, i) = su; U(j, j) = cu;
-        S = U * S;
-        ## Rotation related with V'
-        i = fix (rand () * nn) + 1;
-        do
-          j = fix (rand () * nn) + 1; 
-        until(j != i)
-        V = sparse (eye (n, n));
-        V(i, i) = cv; V(i, j) = sv;
-        V(j, i) = -sv; V(j, j) = cv;
-        S = S * V;
+
+      Uinit = speye (m);
+      Vinit = speye (n);
+      k = round (d*m*n);
+      while (nnz (S) < k)
+        if (m > 1)
+          ## Construct U randomized rotation matrix
+          rot_angleu = 2 * pi * rand ();
+          cu = cos (rot_angleu); su = sin (rot_angleu);
+          rndtmp = randperm (m, 2);
+          i = rndtmp(1); j = rndtmp(2);
+          U = Uinit;
+          U(i, i) = cu; U(i, j) = -su;
+          U(j, i) = su; U(j, j) = cu;
+          S = U * S;
+        endif
+        if (n > 1)
+          ## Construct V' randomized rotation matrix
+          rot_anglev = 2 * pi * rand ();
+          cv = cos (rot_anglev); sv = sin (rot_anglev);
+          rndtmp = randperm (n, 2);
+          i = rndtmp(1); j = rndtmp(2);
+          V = Vinit;
+          V(i, i) = cv;  V(i, j) = sv;
+          V(j, i) = -sv; V(j, j) = cv;
+          S = S * V;
+        endif
       endwhile
     endif
   endif
+
 endfunction
 

--- a/scripts/sparse/sprand.m	Sat Mar 22 13:23:41 2014 +0100
+++ b/scripts/sparse/sprand.m	Sun Mar 23 20:35:22 2014 -0700
@@ -21,21 +21,23 @@
 
 ## -*- texinfo -*-
 ## @deftypefn  {Function File} {} sprand (@var{m}, @var{n}, @var{d})
-## @deftypefnx  {Function File} {} sprand (@var{m}, @var{n}, @var{d}, @var{rc})
+## @deftypefnx {Function File} {} sprand (@var{m}, @var{n}, @var{d}, @var{rc})
 ## @deftypefnx {Function File} {} sprand (@var{s})
-## Generate a random sparse matrix.  The size of the matrix will be
-## @var{m} by @var{n}, with a density of values given by @var{d}.
-## @var{d} should be between 0 and 1.  Values will be uniformly
-## distributed between 0 and 1.
+## Generate a sparse matrix with uniformly distributed random values.
+##
+## The size of the matrix is @var{m}x@var{n} with a density of values @var{d}.
+## @var{d} must be between 0 and 1.  Values will be uniformly distributed on
+## the interval (0, 1).
 ##
-## If called with a single matrix argument, a random sparse matrix is
-## generated wherever the matrix @var{S} is non-zero.
+## If called with a single matrix argument, a sparse matrix is generated with
+## random values wherever the matrix @var{s} is non-zero.
 ##
-## If called with the rc parameter as a scalar, a random sparse matrix with a
-## reciprocal condition number rc is generated. If rc is a vector, then it 
-## contains the first singular values of the matrix generated (length(rc) <= min(m, n)).
+## If called with a scalar fourth argument @var{rc}, a random sparse matrix
+## with reciprocal condition number @var{rc} is generated.  If @var{rc} is
+## a vector, then it specifies the first singular values of the generated
+## matrix (@code{length (@var{rc}) <= min (@var{m}, @var{n})}).
 ##
-## @seealso{sprandn, sprandsym}
+## @seealso{sprandn, sprandsym, rand}
 ## @end deftypefn
 
 ## Author: Paul Kienzle <pkienzle@users.sf.net>
@@ -48,14 +50,14 @@
 ## David Bateman
 ##      2004-10-20      Texinfo help and copyright message
 
-function S = sprand (m, n, d, rc)
+function s = sprand (m, n, d, rc)
 
   if (nargin == 1 )
-    S = __sprand_impl__ (m, @rand);
+    s = __sprand_impl__ (m, @rand);
   elseif ( nargin == 3)
-    S = __sprand_impl__ (m, n, d, "sprand", @rand);
+    s = __sprand_impl__ (m, n, d, "sprand", @rand);
   elseif (nargin == 4)
-    S = __sprand_impl__ (m, n, d, rc, "sprand", @rand);
+    s = __sprand_impl__ (m, n, d, rc, "sprand", @rand);
   else
     print_usage ();
   endif
@@ -63,22 +65,15 @@
 endfunction
 
 
+%% Test 3-input calling form
 %!test
 %! s = sprand (4, 10, 0.1);
 %! assert (size (s), [4, 10]);
 %! assert (nnz (s) / numel (s), 0.1);
 
-%% Test 1-input calling form
-%!test
-%! s = sprand (sparse ([1 2 3], [3 2 3], [2 2 2]));
-%! [i, j, v] = find (s);
-%! assert (sort (i), [1 2 3]');
-%! assert (sort (j), [2 3 3]');
-%! assert (all (v > 0 & v < 1));
-
 %% Test 4-input calling form
 %!test
-%! d = rand();
+%! d = rand ();
 %! s1 = sprand (100, 100, d, 0.4);
 %! rc = [5, 4, 3, 2, 1, 0.1];
 %! s2 = sprand (100, 100, d, rc);
@@ -89,22 +84,31 @@
 %! assert (nnz (s2) / (100*100), d, 0.02); 
 %! assert (svd (s3)', [5 4 3 2], sqrt (eps));
 
+%% Test 1-input calling form
+%!test
+%! s = sprand (sparse ([1 2 3], [3 2 3], [2 2 2]));
+%! [i, j, v] = find (s);
+%! assert (sort (i), [1 2 3]');
+%! assert (sort (j), [2 3 3]');
+%! assert (all (v > 0 & v < 1));
+
 %% Test input validation
 %!error sprand ()
 %!error sprand (1, 2)
 %!error sprand (1, 2, 3, 4)
-%!error sprand (ones (3), 3, 0.5)
-%!error sprand (3.5, 3, 0.5)
-%!error sprand (0, 3, 0.5)
-%!error sprand (3, ones (3), 0.5)
-%!error sprand (3, 3.5, 0.5)
-%!error sprand (3, 0, 0.5)
-%!error sprand (3, 3, -1)
-%!error sprand (3, 3, 2)
-%!error sprand (2, 2, 0.2, -1)
-%!error sprand (2, 2, 0.2, 2)
+%!error <M must be an integer greater than 0> sprand (ones (3), 3, 0.5)
+%!error <M must be an integer greater than 0> sprand (3.5, 3, 0.5)
+%!error <M must be an integer greater than 0> sprand (0, 3, 0.5)
+%!error <N must be an integer greater than 0> sprand (3, ones (3), 0.5)
+%!error <N must be an integer greater than 0> sprand (3, 3.5, 0.5)
+%!error <N must be an integer greater than 0> sprand (3, 0, 0.5)
+%!error <D must be between 0 and 1> sprand (3, 3, -1)
+%!error <D must be between 0 and 1> sprand (3, 3, 2)
+%!error <RC must be a scalar or vector> sprand (2, 2, 0.2, ones (3,3))
+%!error <RC must be between 0 and 1> sprand (2, 2, 0.2, -1)
+%!error <RC must be between 0 and 1> sprand (2, 2, 0.2, 2)
 
 %% Test very large, very low density matrix doesn't fail 
 %!test
-%! s = sprand(1e6,1e6,1e-7);
+%! s = sprand (1e6, 1e6, 1e-7);
 

--- a/scripts/sparse/sprandn.m	Sat Mar 22 13:23:41 2014 +0100
+++ b/scripts/sparse/sprandn.m	Sun Mar 23 20:35:22 2014 -0700
@@ -23,32 +23,33 @@
 ## @deftypefn  {Function File} {} sprandn (@var{m}, @var{n}, @var{d})
 ## @deftypefnx {Function File} {} sprandn (@var{m}, @var{n}, @var{d}, @var{rc})
 ## @deftypefnx {Function File} {} sprandn (@var{s})
-## Generate a random sparse matrix.  The size of the matrix will be
-## @var{m} by @var{n}, with a density of values given by @var{d}.
-## @var{d} should be between 0 and 1.  Values will be normally
-## distributed with mean of zero and variance 1.
+## Generate a sparse matrix with normally distributed random values.
+##
+## The size of the matrix is @var{m}x@var{n} with a density of values @var{d}.
+## @var{d} must be between 0 and 1.  Values will be normally distributed with a
+## mean of 0 and a variance of 1.
 ##
-## If called with a single matrix argument, a random sparse matrix is
-## generated wherever the matrix @var{S} is non-zero.
+## If called with a single matrix argument, a sparse matrix is generated with
+## random values wherever the matrix @var{s} is non-zero.
+##
+## If called with a scalar fourth argument @var{rc}, a random sparse matrix
+## with reciprocal condition number @var{rc} is generated.  If @var{rc} is
+## a vector, then it specifies the first singular values of the generated
+## matrix (@code{length (@var{rc}) <= min (@var{m}, @var{n})}).
 ## 
-## If called with the rc parameter, then a random sparse matrix with a
-## reciprocal condition number rc is generated if rc is scalar. If rc 
-## is a vector, then it contains the first singular values of the matrix
-## generated (length(rc) <= min(m, n)).
-## 
-## @seealso{sprand, sprandsym}
+## @seealso{sprand, sprandsym, randn}
 ## @end deftypefn
 
 ## Author: Paul Kienzle <pkienzle@users.sf.net>
 
-function S = sprandn (m, n, d, rc)
+function s = sprandn (m, n, d, rc)
 
   if (nargin == 1 )
-    S = __sprand_impl__ (m, @randn);
+    s = __sprand_impl__ (m, @randn);
   elseif ( nargin == 3)
-    S = __sprand_impl__ (m, n, d, "sprandn", @randn);
+    s = __sprand_impl__ (m, n, d, "sprandn", @randn);
   elseif (nargin == 4)
-    S = __sprand_impl__ (m, n, d, rc, "sprandn", @randn);
+    s = __sprand_impl__ (m, n, d, rc, "sprandn", @randn);
   else
     print_usage ();
   endif
@@ -56,21 +57,15 @@
 endfunction
 
 
+%% Test 3-input calling form
 %!test
 %! s = sprandn (4, 10, 0.1);
 %! assert (size (s), [4, 10]);
 %! assert (nnz (s) / numel (s), 0.1);
 
-%% Test 1-input calling form
-%!test
-%! s = sprandn (sparse ([1 2 3], [3 2 3], [2 2 2]));
-%! [i, j] = find (s);
-%! assert (sort (i), [1 2 3]');
-%! assert (sort (j), [2 3 3]');
-
 %% Test 4-input calling form
 %!test
-%! d = rand();
+%! d = rand ();
 %! s1 = sprandn (100, 100, d, 0.4);
 %! rc = [5, 4, 3, 2, 1, 0.1];
 %! s2 = sprandn (100, 100, d, rc);
@@ -81,22 +76,30 @@
 %! assert (nnz (s2) / (100*100), d, 0.02); 
 %! assert (svd (s3)', [5 4 3 2], sqrt (eps));
 
+%% Test 1-input calling form
+%!test
+%! s = sprandn (sparse ([1 2 3], [3 2 3], [2 2 2]));
+%! [i, j] = find (s);
+%! assert (sort (i), [1 2 3]');
+%! assert (sort (j), [2 3 3]');
+
 %% Test input validation
 %!error sprandn ()
 %!error sprandn (1, 2)
 %!error sprandn (1, 2, 3, 4)
-%!error sprandn (ones (3), 3, 0.5)
-%!error sprandn (3.5, 3, 0.5)
-%!error sprandn (0, 3, 0.5)
-%!error sprandn (3, ones (3), 0.5)
-%!error sprandn (3, 3.5, 0.5)
-%!error sprandn (3, 0, 0.5)
-%!error sprandn (3, 3, -1)
-%!error sprandn (3, 3, 2)
-%!error sprandn (2, 2, 0.2, -1)
-%!error sprandn (2, 2, 0.2, 2)
+%!error <M must be an integer greater than 0> sprandn (ones (3), 3, 0.5)
+%!error <M must be an integer greater than 0> sprandn (3.5, 3, 0.5)
+%!error <M must be an integer greater than 0> sprandn (0, 3, 0.5)
+%!error <N must be an integer greater than 0> sprandn (3, ones (3), 0.5)
+%!error <N must be an integer greater than 0> sprandn (3, 3.5, 0.5)
+%!error <N must be an integer greater than 0> sprandn (3, 0, 0.5)
+%!error <D must be between 0 and 1> sprandn (3, 3, -1)
+%!error <D must be between 0 and 1> sprandn (3, 3, 2)
+%!error <RC must be a scalar or vector> sprandn (2, 2, 0.2, ones (3,3))
+%!error <RC must be between 0 and 1> sprandn (2, 2, 0.2, -1)
+%!error <RC must be between 0 and 1> sprandn (2, 2, 0.2, 2)
 
 %% Test very large, very low density matrix doesn't fail 
 %!test
-%! s = sprandn(1e6,1e6,1e-7);
+%! s = sprandn (1e6,1e6,1e-7);