||Stochastic (block) BFGS method for solving the
empirical risk minimization problem with logistic loss and L2
regularizer. Related paper.
||A suite of randomized methods for inverting
positive definite matrices implemented in MATLAB. Related paper.
||A lab for testing and comparing randomized
methods for solving linear systems. Implemented in MATLAB. Related paper.
||A framework for communication-efficient
distributed optimization for machine learning.
Accelerated, Parallel and PROXimal coordinate descent. This
is an efficient C++ code based on this paper. We also
implement PCDM (parallel coordinate descent), SDCA (stochastic dual
coordinate ascent) and AGD (Accelerated Gradient Descent).
Semi-stochastic gradient descent method for fast training of L2 regularized logistic regression. This is an efficient C++ code (can be called from MATLAB), based on this paper.
|Serial [1 5], parallel [2 3 4] and distributed [6 7] coordinate descent code for big data optimization. The parallel and distributed codes can solve LASSO instances with terabyte matrices and billions of features, and are scalable.|
Associate Professor (Reader)
Fellow in Mathematical Sciences