diff --git a/pyrival/linear_algebra/Cholesky b/pyrival/linear_algebra/Cholesky
new file mode 100644
index 0000000..1980504
--- /dev/null
+++ b/pyrival/linear_algebra/Cholesky
@@ -0,0 +1,131 @@
+from math import *
+
+class matrix:
+    
+    # implements basic operations of a matrix class
+    
+    def __init__(self, value):
+        self.value = value
+        self.dimx = len(value)
+        self.dimy = len(value[0])
+        if value == [[]]:
+            self.dimx = 0
+    
+    def zero(self, dimx, dimy):
+        # check if valid dimensions
+        if dimx < 1 or dimy < 1:
+            raise ValueError, "Invalid size of matrix"
+        else:
+            self.dimx = dimx
+            self.dimy = dimy
+            self.value = [[0 for row in range(dimy)] for col in range(dimx)]
+    
+    def identity(self, dim):
+        # check if valid dimension
+        if dim < 1:
+            raise ValueError, "Invalid size of matrix"
+        else:
+            self.dimx = dim
+            self.dimy = dim
+            self.value = [[0 for row in range(dim)] for col in range(dim)]
+            for i in range(dim):
+                self.value[i][i] = 1
+    
+    def show(self):
+        for i in range(self.dimx):
+            print self.value[i]
+        print ' '
+    
+    def __add__(self, other):
+        # check if correct dimensions
+        if self.dimx != other.dimx or self.dimy != other.dimy:
+            raise ValueError, "Matrices must be of equal dimensions to add"
+        else:
+            # add if correct dimensions
+            res = matrix([[]])
+            res.zero(self.dimx, self.dimy)
+            for i in range(self.dimx):
+                for j in range(self.dimy):
+                    res.value[i][j] = self.value[i][j] + other.value[i][j]
+            return res
+    
+    def __sub__(self, other):
+        # check if correct dimensions
+        if self.dimx != other.dimx or self.dimy != other.dimy:
+            raise ValueError, "Matrices must be of equal dimensions to subtract"
+        else:
+            # subtract if correct dimensions
+            res = matrix([[]])
+            res.zero(self.dimx, self.dimy)
+            for i in range(self.dimx):
+                for j in range(self.dimy):
+                    res.value[i][j] = self.value[i][j] - other.value[i][j]
+            return res
+    
+    def __mul__(self, other):
+        # check if correct dimensions
+        if self.dimy != other.dimx:
+            raise ValueError, "Matrices must be m*n and n*p to multiply"
+        else:
+            # subtract if correct dimensions
+            res = matrix([[]])
+            res.zero(self.dimx, other.dimy)
+            for i in range(self.dimx):
+                for j in range(other.dimy):
+                    for k in range(self.dimy):
+                        res.value[i][j] += self.value[i][k] * other.value[k][j]
+            return res
+    
+    def transpose(self):
+        # compute transpose
+        res = matrix([[]])
+        res.zero(self.dimy, self.dimx)
+        for i in range(self.dimx):
+            for j in range(self.dimy):
+                res.value[j][i] = self.value[i][j]
+        return res
+    
+    def Cholesky(self, ztol=1.0e-5):
+        # Computes the upper triangular Cholesky factorization of
+        # a positive definite matrix.
+        res = matrix([[]])
+        res.zero(self.dimx, self.dimx)
+        
+        for i in range(self.dimx):
+            S = sum([(res.value[k][i])**2 for k in range(i)])
+            d = self.value[i][i] - S
+            if abs(d) < ztol:
+                res.value[i][i] = 0.0
+            else:
+                if d < 0.0:
+                    raise ValueError, "Matrix not positive-definite"
+                res.value[i][i] = sqrt(d)
+            for j in range(i+1, self.dimx):
+                S = sum([res.value[k][i] * res.value[k][j] for k in range(self.dimx)])
+                if abs(S) < ztol:
+                    S = 0.0
+                res.value[i][j] = (self.value[i][j] - S)/res.value[i][i]
+        return res
+    
+    def CholeskyInverse(self):
+        # Computes inverse of matrix given its Cholesky upper Triangular
+        # decomposition of matrix.
+        res = matrix([[]])
+        res.zero(self.dimx, self.dimx)
+        
+        # Backward step for inverse.
+        for j in reversed(range(self.dimx)):
+            tjj = self.value[j][j]
+            S = sum([self.value[j][k]*res.value[j][k] for k in range(j+1, self.dimx)])
+            res.value[j][j] = 1.0/tjj**2 - S/tjj
+            for i in reversed(range(j)):
+                res.value[j][i] = res.value[i][j] = -sum([self.value[i][k]*res.value[k][j] for k in range(i+1, self.dimx)])/self.value[i][i]
+        return res
+    
+    def inverse(self):
+        aux = self.Cholesky()
+        res = aux.CholeskyInverse()
+        return res
+    
+    def __repr__(self):
+        return repr(self.value)