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搜索结果: 1-15 共查到optimization相关记录20条 . 查询时间(0.125 秒)
Communication compression is an essential strategy for alleviating communication overhead by reducing the volume of information exchanged between computing nodes in large-scale distributed stochastic ...
In the first part, I shall present a new way to construct highly stiff stable schemes. Traditional time discretization schemes are usually based on Taylor expansions at $t_{n+\beta}$ with $\beta\in [0...
In this talk, we will introduce some optimization problem in remote sensing data processing. The high dimensional characteristics of remote sensing data, especially hyperspectral data, will not only l...
In this talk, we discuss a unifying deep unfolding multi-sampling-ratio interpretable CS-MRI framework. The combined approach offers more generalizability than the existing deep-learning-based CS-MRI ...
We focus on the nonconvex-strongly-convex bilevel optimization problem (BLO). In this BLO, the objective function of the upper-level problem is nonconvex and possibly nonsmooth, and the lower-level pr...
Tensor-based modeling and computation emerge prominently with urgent demands from practical applications in the big data era. With the intrinsic sparsity in real data sets and the dimensionality reduc...
Sparsity is a naturally occurring characteristic in many real-world applications including signal denoising, outlier detection, and finance. On one hand, sparsity assumption allows people to tackle in...
Zero-One Composite Optimization (0/1-COP) is a prototype of nonsmooth, non- convex optimization problems and it has attracted much attention recently. Augmented Lagrangian Method (ALM) has stood out a...
Nonconvex constrained optimization (NCO) has been one of the important research fields in optimization community. It has widely appeared in many application fields. However, challenges for solving NCO...
It is a relatively recent discovery in geometric topology that optimization problems of certain topological complexity are connected to important geometric and topological information. One example is ...
In this talk, we consider a distributed interval optimization problem (DIOP) with uncertainties, whose global function is formed by local convex interval functions. In seeking Pareto solutions for dis...
Along with the rapid development of artificial intelligence (AI) technology, scientific research enters a new era of AI. Topology optimization (TO) and AI technology are recently showing a growing tre...
In electronic structure calculations, Kohn-Sham equations rank among the most widely adopted mathematical models. However, due to the deficiency of available approximations for exchange-correlation en...
Modern neural networks are usually over-parameterized—the number of parameters exceeds the number of training data. In this case the loss functions tend to have many (or even infinite) global minima, ...
Unlike traditional robust optimization, personalized optimization aims to find the values of control variables that yield the optimal value of the objective function for given values of environmental ...

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