Skip to main content

Linear Algebra - Matrices & Vectors

 A matrix is a rectangular array of numbers or functions which will be enclosed in brackets. Matrix has rows, horizontal lines of entries and columns, vertical lines of entries. A matrix of any size m x n is called a rectangular matrix.

Square matrix, has as many rows as columns. Matrices of different sizes cannot be added. Matrix addition is commutative and associative.

Matrix having just a single row or column is called vectors. Row vector has one row and Column vector has one Column.

Matrix Multiplication

Transposition

The transpose of a matrix is obtained by writing its rows as columns (or equivalently its columns as rows). Transposition converts row vectors to column vectors and vice versa.

Rules of Transposition

Symmetric Matrix

Square matrix whose transpose equals to the matrix itself.

Skew Symmetric Matrix

Square matrix whose transpose equals to the minus of matrix.

Upper Triangle Matrix

Square matrix having non zero entries on and above the main diagonal.

Lower Triangle Matrix

Square matrix having non zero entries on and below the main diagonal.

Diagonal Matrix

Square matrix having non zero entries only on the main diagonal.

Unit Matrix

Diagonal matrix having all its main diagonal element are 1.

Stochastic Matrix

Square matrix with all entries non negative and all columns sums equal to 1.

Matrices are used to solve systems of linear equations. The approach to solving linear systems is called the Gauss elimination method.

The system is called linear because each variable x appears in the first power only, just as in the equation of a straight line.

x - variable ; a - coefficient ; b - numbers

If all the b are zero, then the system is called a homogeneous system. If at least one b is not zero, then the system is called a nonhomogeneous system.

Popular posts from this blog

Exercise 2 - Amdahl's Law

A programmer has parallelized 99% of a program, but there is no value in increasing the problem size, i.e., the program will always be run with the same problem size regardless of the number of processors or cores used. What is the expected speedup on 20 processors? Solution As per Amdahl's law, the speedup,  N - No of processors = 20 f - % of parallel operation = 99% = 1 / (1 - 0.99) + (0.99 / 20) = 1 / 0.01 + (0.99 / 20) = 16.807 The expected speedup on 20 processors is 16.807

Gaussian Elimination - Row reduction Algorithm

 Gaussian elimination is a method for solving matrix equations of the form, Ax=b.  This method is also known as the row reduction algorithm. Back  Substitution Solving the last equation for the variable and then work backward into the first equation to solve it.  The fundamental idea is to add multiples of one equation to the others in order to eliminate a variable and to continue this process until only one variable is left. Pivot row The row that is used to perform elimination of a variable from other rows is called the pivot row. Example: Solving a linear equation The augmented matrix for the above equation shall be The equation shall be solved using back substitution. The eliminating the first variable (x1) in the first row (Pivot row) by carrying out the row operation. As the second row become zero, the row will be shifted to bottom by carrying out partial pivoting. Now, the second variable (x2)  shall be eliminated by carrying out the row operation again. ...

Decision Tree Scalability Methods

 The scalable decision tree induction methods are RainForest and BOAT. RainForest method maintains an AVC set for each attribute at each node. AVC stands for Attribute Value Classlabel. BOAT , stands for Bootstrapped Optimistic Algorithm for Tree construction, uses a statistical technique known as bootstrapping., by which several smaller subsets are created. The several trees are created using the subsets and finally the full tree is generated using the trees created by smaller subsets. BOAT was found to be two to three times faster than RainForest.