An Efficient Preconditioner for Stochastic Gradient Descent Optimization of Image Registration.Stochastic gradient descent (SGD) is commonly used to solve (parametric) image registration problems.  In the case of badly scaled problems, SGD, however, only exhibits sublinear convergence properties.  In this paper, we propose an efficient preconditioner estimation method to improve the convergence rate of SGD.  Experiments on different clinical datasets show that the proposed method, indeed, improves the convergence rate compared with SGD with speedups around 2~5 in all tested settings while retaining the same level of registration accuracy.
SGD
An Efficient Preconditioner for Stochastic Gradient Descent Optimization of Image Registration.Stochastic gradient descent (SGD) is commonly used to solve (parametric) image registration problems.  In the case of badly scaled problems, SGD, however, only exhibits sublinear convergence properties.  In this paper, we propose an efficient preconditioner estimation method to improve the convergence rate of SGD.  Experiments on different clinical datasets show that the proposed method, indeed, improves the convergence rate compared with SGD with speedups around 2~5 in all tested settings while retaining the same level of registration accuracy.
SGD