LE T H A; LE H M; DINH T P.Feature selection in machine learning: an exact penalty approach using a difference of convex function algorithm.Machine Learning.2015.163-186Le Thi, H.A., Le, H.M., Pham, D.T.: Featur
炼丹魔法书-Convex Optimization for Machine Learning 这本书是由 Michael Nielsen 和 Isaac Schreiber 合著的，于2019年由MIT出版社出版。该书是机器学习领域中关于非凸优化问题的经典著作之一，主要介绍了一些非凸优化算法以及如何求解非凸优化问题。书中主要讲了两种非凸情况：一是目标函数是凸的，约束集合不是凸的...
However, if you cross the function line, then the function is non-convex. A non-convex function As you can see in the figure above, the red line crosses the function, which means it is non-convex. Note, however, that the function is convex on some intervals, for instance on [-1,+1...
We propose a combination of machine learning and flux limiting for property-preserving subgrid scale modeling in the context of flux-limited finite volume methods for the one-dimensional shallow-water equations. The numerical fluxes of a conservative target scheme are fitted to the coarse-mesh ...
2020,Machine Learning (Second Edition) Review article Multi-objective optimization for spectrum sharing in cognitive radio networks: A review 4.5Convex optimization A power control problem inCRNsis formulated as anMOO problem, through developing a convexcost function, based onSINRand transmit power of ...
Differentiable Quasiconvex Function Strictly Quasiconvex Function Strongly Quasiconvex Function Pseudoconvex Function Convex Programming Problem Fritz-John Conditions Karush-Kuhn-Tucker Optimality Necessary Conditions Algorithms for Convex Problems Convex Optimization - Quick Guide Convex Optimization - Resources Co...
Many problems in machine learning and other fields can be (re)formulated as linearly constrained separable convex programs. In most of the cases, there are
定义1(Convex function):一个函数 f 被称为凸函数,如果对任意 x,y ,满足 (0.1)f(y)≥f(x)+⟨∇f(x),y−x⟩ 定义2(Strong convexity):一个函数 f 被称为 μ -强凸函数,如果对任意 x,y ,满足 (0.2)f(y)≥f(x)+⟨∇f(x),y−x⟩+μ2‖y−x‖2 (别小看后面多出...
Nan W., et al. “The Value of Collaboration in Convex Machine Learning with Differential Privacy.” 2020 IEEE Symposium on Security and Privacy. 304-317. 联邦学习场景中,在适应度函数平滑、强凸、利普斯特连续的条件下,估算各客户端使用不同隐私预算时最终全局模型的信息损失量。实践中,针对适应...
layer feedforward networks (SLFNs) with randomly generated additive or radial basis function (RBF) hidden nodes (according to any continuous sampling distribution) can work as universal approximators and the resulting incremental extreme learning machine (I-ELM) outperforms many popular learning ...