bug: small fixes

Author: mrava87

pylops/optimization/basic.py

@@ -155,10 +155,6 @@ def cgls(
         Equal to ``r1norm`` if :math:`\epsilon=0`
     cost : :obj:`numpy.ndarray`, optional
         History of r1norm through iterations
-    preallocate : :obj:`bool`, optional
-            .. versionadded:: 2.5.0
-
-            Pre-allocate all variables used by the solver
 
     Notes
     -----

pylops/optimization/cls_basic.py

@@ -922,22 +922,22 @@ def setup(
             self.u = y.copy()
         else:
             x = x0.copy()
-            if self.preallocate:
+            if not self.preallocate:
                 self.u = self.y - self.Op.matvec(x0)
             else:
                 self.u = self.ncp.empty_like(self.y)
                 self.ncp.subtract(self.y, self.Op.matvec(x0), out=self.u)
         self.alfa = 0.0
         self.beta = self.ncp.linalg.norm(self.u)
         if self.beta > 0.0:
-            if self.preallocate:
+            if not self.preallocate:
                 self.u = self.u / self.beta
             else:
                 self.ncp.divide(self.u, self.beta, out=self.u)
             self.v = self.Op.rmatvec(self.u)
             self.alfa = self.ncp.linalg.norm(self.v)
             if self.alfa > 0:
-                if self.preallocate:
+                if not self.preallocate:
                     self.v = self.v / self.alfa
                 else:
                     self.ncp.divide(self.v, self.alfa, out=self.v)

pylops/optimization/cls_sparsity.py

@@ -475,10 +475,10 @@ def _step_data(self, x: NDArray, engine: str = "scipy", **kwargs_solver) -> NDAr
             )[0]
         else:
             # other iterations (weighted least-squares)
-            if not self.preallocate and self.iiter == 1:
+            if self.preallocate and self.iiter == 1:
                 self.rw = self.ncp.zeros_like(self.y)
 
-            if self.preallocate:
+            if not self.preallocate:
                 if self.threshR:
                     self.rw = 1.0 / self.ncp.maximum(self.ncp.abs(self.r), self.epsR)
                 else:
@@ -538,9 +538,9 @@ def _step_model(
                 )
         else:
             # other iterations (weighted least-squares)
-            if not self.preallocate and self.iiter == 1:
+            if self.preallocate and self.iiter == 1:
                 self.rw = self.ncp.zeros_like(x)
-            if self.preallocate:
+            if not self.preallocate:
                 self.rw = self.ncp.abs(x)
                 self.rw = self.rw / self.rw.max()
             else: