后一类可能不太好理解:如果说前一类对应的为gradient descent算法的话,那么后一类优化问题对应的一种特殊情况是 projected gradient descent。因为强化学习里面还是会遇到这种要做 projection 的情形的(比如考虑一个 direct parameterization 或者说 tabular case),因此我也把相关结论抄了一下以备后用。 除此之外,还考虑...
nonlinear equations/ accelerated projected steepest descent methodnonlinear inverse problemssparsity constraintsiterative algorithmiteration schemesprojected gradient methodThis paper is concerned with the construction of an iterative algorithm to solve nonlinear inverse problems with an lconstraint on x. One ...
We also propose accelerated versions of this iterative method, using ingredients of the (linear) steepest descent method. We prove convergence in norm for one of these projected gradient methods, without and with acceleration. 展开 关键词: Mathematics - Numerical Analysis DOI: 10.1007/s00041-008-...
Zhang, C., Chen, X.: Smoothing projected gradient method and its application to stochastic linear complementarity problems. SIAM J. Optim. 20(2), 627–649 (2009) MathSciNet MATH Google Scholar Zhang, C., Chen, X.: A smoothing active set method for linearly constrained non-Lipschitz non...
Shamir O, Zhang T (2013) Stochastic gradient descent for non-smooth optimization: Convergence results and optimal averaging schemes. In: Proceedings of the 30th International Conference on Machine Learning, 28, 71–79 Ghadimi S, Lan G (2013) Stochastic first- and zeroth-order methods for nonconv...
it is important to explore ways to increase tracking robustness further, such as for example using the optical flow between video frames to link key-points together in multi-animal tracking15, using a 3D convolutional neural network to detect body key-points by considering ‘un-projected’ views...
We also propose accelerated versions of this iterative method, using ingredients of the (linear) steepest descent method. We prove convergence in norm for one of these projected gradient methods, without and with acceleration.Ingrid Daubechies
We discuss here the strongly convex situation, and how 'fast' methods can be derived by adapting the overrelaxation strategy of Nesterov for projected gradient descent. We also investigate slightly more general alternating descent methods, where several descent steps in each variable are alternatively ...
We also propose accelerated versions of this iterative method, using ingredients of the (linear) steepest descent method. We prove convergence in norm for one of these projected gradient methods, without and with acceleration.doi:10.1007/s00041-008-9039-8Ingrid Daubechies...
Minimization was carried out through 1000 steps of the conjugated gradient method followed by 2000 steps of steepest descent. The temperature of the system was raised from 0 to 298 K via 50 ps of simulation with constant volume/constant temperature in the canonical ensemble (NVT), with a 5 ...