In order to capture the learning and prediction problems accurately, structural constraints such as sparsity or low rank are frequently imposed or else the objective itself is designed to be a non-convex function. This is especially true of algorithms that operate in high-dimensional spaces or ...
Non-convex optimization is ubiquitous in modern machine learning: recent breakthroughs in deep learning require optimizing non-convex training objective functions; problems that admit accurate convex relaxation can often be solved more efficiently with non-convex formulations. However, the theoretical understa...
2017.Allrightsreserved.AbstractNon-convexoptimizationisubiquitousinmodernmachinelearning:recentbreak-throughsindeeplearningrequireoptimizingnon-convextrainingobjectivefunctions;problemsthatadmitaccurateconvexrelaxationcanoftenbesolvedmoreefficientlywithnon-convexformulations.However,thetheoreticalunderstandingofnon-convex...
丛书:Foundations and Trends® in Machine Learning ISBN:9781680833690 豆瓣评分 目前无人评价 内容简介· ··· Prateek Jain and Purushottam Kar (2017), "Non-convex Optimization for Machine Learning", Foundations and Trends® in Machine Learning: Vol. 10: No. 3-4, pp 142-363. http://dx.d...
In order to capture the learning and prediction problems accurately, structural constraints such as sparsity or low rank are frequently imposed or else the objective itself is designed to be a non-convex function. This is especially true of algorithms that operate in high-dimensional spaces or ...
接下来,我分两个情况来讨论收敛性:1.Convex。2. Strongly convex。 1.1.convex case 定理1.1(Nonsmooth + convex)如果函数 f 是凸的且是Lipschitzness的。对于迭代方法(1.1),步长选择策略为: \alpha_k =\frac{f(x^k) - f^*}{\|g^k\|^2} 如果g^k \neq 0 ,否则 \alpha_k = 1 。那么我们有:...
作者提出了一个基于Moreau envelope的merit function。通过这个技术，他们对一系列非光滑的随机算法给出了...
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Deep learningConvolutional neural networkRegularization methods are often employed to reduce overfitting of machine learning models. Nonconvex penalty functions are often considered for regularization because of their near-unbiasedness properties. In this paper, we consider two relatively new penalty functions...
At each time step in the environment, MPC solves the non-convex optimization problem x⋆1:T,u⋆1:T=argminx1...This Library: A Differentiable PyTorch MPC Layer We ...