#stochastic average gradient (SAG) is up next

[tagitables]

Liblinear:

Description: liblinear is a library for large-scale linear classification. It supports logistic regression and linear Support Vector Machines (SVMs). It's designed for efficiency with large datasets and is particularly well-suited for sparse data.

Algorithm: It typically uses a coordinate descent algorithm, which is efficient for high-dimensional data. The liblinear solver works well when you have a lot of features relative to the number of samples.

Use Cases: It��s often used when you have a large amount of data or when working with sparse data matrices (e.g., text classification).

Newton-CG:

Description: Newton-CG (Newton Conjugate Gradient) is an optimization algorithm that applies the Newton's method for finding the minimum of a function. It combines the Newton’s method with the Conjugate Gradient method to handle large-dimensional problems more efficiently.

Algorithm: This solver uses second-order information (the Hessian matrix) to guide the optimization process, making it potentially faster and more accurate, but also more computationally expensive.

Use Cases: It’s suitable for scenarios where you can afford the computational cost and need precise convergence, especially for logistic regression and other generalized linear models.

Newton-Cholesky:

Description: Newton-Cholesky is a variant of the Newton's method for optimization that leverages Cholesky decomposition to solve the system of linear equations involved in the Newton step.

Algorithm: The Cholesky decomposition helps to solve the system of equations more efficiently by decomposing the Hessian matrix into a product of a lower triangular matrix and its transpose. This can make the method more efficient for certain problems.

Use Cases: It’s typically used in scenarios where you need the precision of Newton’s method but also want to take advantage of efficient matrix factorizations to handle larger problems.