What are the types of gradient descent algorithm?
There are three types of gradient descent learning algorithms: batch gradient descent, stochastic gradient descent and mini-batch gradient descent.
- Batch gradient descent.
- Stochastic gradient descent.
- Mini-batch gradient descent.
What is Sprand function in MATLAB?
R = sprand( S ) creates a sparse matrix that has the same sparsity pattern as the matrix S , but with uniformly distributed random entries.
What does PINV do in Matlab?
pinv treats singular values of A that are smaller than the tolerance as zero.
What is difference between gradient descent and stochastic gradient descent?
The only difference comes while iterating. In Gradient Descent, we consider all the points in calculating loss and derivative, while in Stochastic gradient descent, we use single point in loss function and its derivative randomly.
What is the preconditioned conjugate gradients method?
The preconditioned conjugate gradients method (PCG) was developed to exploit the structure of symmetric positive definite matrices. Several other algorithms can operate on symmetric positive definite matrices, but PCG is the quickest and most reliable at solving those types of systems [1] .
What is conjugate gradients squared algorithm?
Conjugate Gradients Squared Method The conjugate gradients squared (CGS) algorithm was developed as an improvement to the biconjugate gradient (BiCG) algorithm. Instead of using the residual and its conjugate, the CGS algorithm avoids using the transpose of the coefficient matrix by working with a squared residual [1].
How to solve a preconditioner matrix?
To aid with the slow convergence, you can specify a preconditioner matrix. Since A is symmetric, use ichol to generate the preconditioner M = L L T. Solve the preconditioned system by specifying L and L’ as inputs to pcg.
What are preconditioner matrices in PCG?
Preconditioner matrices, specified as separate arguments of matrices or function handles. You can specify a preconditioner matrix M or its matrix factors M = M1*M2 to improve the numerical aspects of the linear system and make it easier for pcg to converge quickly.