diff --git a/lib/type/matrix/util/README.md b/lib/type/matrix/util/README.md
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+Algorithms for the implementation of element wise operations between a Dense and Sparse matrices:
+
+- **Algorithm 1 `x(dense, sparse)`**
+  * Algorithm should clone `DenseMatrix` and call the `x(d(i,j), s(i,j))` operation for the items in the Dense and Sparse matrices (iterating on the Sparse matrix nonzero items), updating the cloned matrix.
+  * Output type is a `DenseMatrix` (the cloned matrix)
+  * `x()` operation invoked NZ times (number of nonzero items in `SparseMatrix`)
+
+  **Cij = x(Dij, Sij);    Sij != 0**
+  **Cij = Dij          ;    otherwise**
+
+_ **Algorithm 2 `x(dense, sparse)`**
+  * Algorithm should iterate `SparseMatrix` (nonzero items) and call the `x(d(i,j),s(i,j))` operation for the items in the Sparse and Dense matrices (since zero & X == zero)
+  * Output type is a `SparseMatrix` since the number of nonzero items will be less or equal the number of nonzero elements in the Sparse Matrix.
+  * `x()` operation invoked NZ times (number of nonzero items in `SparseMatrix`)
+
+  **Cij = x(Dij, Sij);    Sij != 0**
+  **Cij = 0          ;    otherwise**
+
+- **Algorithm 3 `x(dense, sparse)`**
+  * Algorithm should iterate `SparseMatrix` (nonzero and zero items) and call the `x(s(i,j),d(i,j))` operation for the items in the Dense and Sparse matrices
+  * Output type is a `DenseMatrix`
+  * `x()` operation invoked M*N times
+
+  **Cij = x(Dij, Sij);    Sij != 0**
+  **Cij = x(Dij, 0);    otherwise**
+
+- **Algorithm 4 `x(sparse, sparse)`**
+  * Algorithm should iterate on the nonzero values of matrices A and B and call `x(Aij, Bij)` when both matrices contain value at (i,j)
+  * Output type is a `SparseMatrix`
+  * `x()` operation invoked NZ times (number of nonzero items at the same (i,j) for both matrices)
+
+  **Cij = x(Aij, Bij);    Aij != 0 && Bij != 0**
+  **Cij = Aij;    Aij != 0**
+  **Cij = Bij;    Bij != 0**
+
+Algorithms for the implementation of element wise operations between a Sparse matrices:
+
+- **Algorithm 5 `x(sparse, sparse)`**
+  * Algorithm should iterate on the nonzero values of matrices A and B and call `x(Aij, Bij)` for every nonzero value.
+  * Output type is a `SparseMatrix`
+  * `x()` operation invoked NZ times (number of nonzero values in A only + number of nonzero values in B only + number of nonzero values in A and B)
+
+  **Cij = x(Aij, Bij);    Aij != 0 || Bij != 0**
+  **Cij = 0;  otherwise**
+
+- **Algorithm 6 `x(sparse, sparse)`**
+  * Algorithm should iterate on the nonzero values of matrices A and B and call `x(Aij, Bij)` when both matrices contain value at (i,j).
+  * Output type is a `SparseMatrix`
+  * `x()` operation invoked NZ times (number of nonzero items at the same (i,j) for both matrices)
+
+  **Cij = x(Aij, Bij);    Aij != 0 && Bij != 0**
+  **Cij = 0;  otherwise**
+
+- **Algorithm 7 `x(sparse, sparse)`**
+  * Algorithm should iterate on all values of matrices A and B and call `x(Aij, Bij)`
+  * Output type is a `DenseMatrix`
+  * `x()` operation invoked MxN times
+
+  **Cij = x(Aij, Bij);**
+
+- **Algorithm 8 `x(sparse, sparse)`**
+  * Algorithm should iterate on the nonzero values of matrices A and B and call `x(Aij, Bij)` when both matrices contain value at (i,j). Use the value from Aij when Bij is zero.
+  * Output type is a `SparseMatrix`
+  * `x()` operation invoked NZ times (number of nonzero items at the same (i,j) for both matrices)
+
+  **Cij = x(Aij, Bij);    Aij != 0 && Bij != 0**
+  **Cij = Aij;  Aij != 0**
+  **Cij = 0;  otherwise**
+
+- **Algorithm 9 `x(sparse, sparse)`**
+  * Algorithm should iterate on the nonzero values of matrices A `x(Aij, Bij)`.
+  * Output type is a `SparseMatrix`
+  * `x()` operation invoked NZA times (number of nonzero items in A)
+
+  **Cij = x(Aij, Bij);    Aij != 0**
+  **Cij = 0;  otherwise**
+  
+  Algorithms for the implementation of element wise operations between a Sparse and Scalar Value:
+
+- **Algorithm 10 `x(sparse, scalar)`**
+  * Algorithm should iterate on the nonzero values of matrix A and call `x(Aij, N)`.
+  * Output type is a `DenseMatrix`
+  * `x()` operation invoked NZ times (number of nonzero items)
+
+  **Cij = x(Aij, N);    Aij != 0**
+  **Cij = N;    otherwise**
+
+- **Algorithm 11 `x(sparse, scalar)`**
+  * Algorithm should iterate on the nonzero values of matrix A and call `x(Aij, N)`.
+  * Output type is a `SparseMatrix`
+  * `x()` operation invoked NZ times (number of nonzero items)
+
+  **Cij = x(Aij, N);    Aij != 0**
+  **Cij = 0;    otherwise**
+
+- **Algorithm 12 `x(sparse, scalar)`**
+  * Algorithm should iterate on the zero and nonzero values of matrix A and call `x(Aij, N)`.
+  * Output type is a `DenseMatrix`
+  * `x()` operation invoked MxN times.
+
+  **Cij = x(Aij, N);    Aij != 0**
+  **Cij = x(0, N);    otherwise**
+  
+  Algorithms for the implementation of element wise operations between a Dense and Dense matrices:
+
+- **Algorithm 13 `x(dense, dense)`
+  * Algorithm should iterate on the values of matrix A and B for all dimensions and call `x(Aij..z,Bij..z)`
+  * Output type is a `DenseMatrix`
+  * `x()` operation invoked Z times, where Z is the number of elements in the matrix last dimension. For two dimensional matrix Z = MxN
+
+  **Cij..z = x(Aij..z, Bij..z)**
+  
+  Algorithms for the implementation of element wise operations between a Dense Matrix and a Scalar Value.
+
+- **Algorithm 14 `x(dense, scalar)`**
+  * Algorithm should iterate on the values of matrix A for all dimensions and call `x(Aij..z, N)`
+  * Output type is a `DenseMatrix`
+  * `x()` operation invoked Z times, where Z is the number of elements in the matrix last dimension. 
+
+  **Cij..z = x(Aij..z, N)**