Currently, we support hetero-lr, homo-lr, hetero-linear regression and hetero-poisson regression. In this folder, we use a unified gradient calculation process template for all hetero linear algorithms.

We also provide a quansi-newton method for hetero-lr and hetero-linear regression.

Stochastic Quansi-Newton¶

When using Newton method, we use the following equation to update gradients.

$\dpi{110}&space;w_{k+1}=w_k-&space;\alpha_k*&space;H^{-1}\triangledown&space;F(w_k)&space;$ where H is Hessian matrix of w.

However, getting Hessian matrix is computational expensive. Thus, a more feasible solution is to use quansi-newton methods. We implement a stochastic quansi-newton method whose process can be shown as below.

For more details, please refer to this paper