STREAMING ALGORITHM FOR SUBMODULAR COVER PROBLEM UNDER ADDITIVE NOISE

2021 ◽  
Author(s):  
Nguyen Thi Bich Ngan ◽  
Tran Huu Loi ◽  
Nguyen Dinh Thin ◽  
Pham Nguyen Huy Phuong
2021 ◽  
Author(s):  
Bich-Ngan T. Nguyen ◽  
Phuong N. H. Pham ◽  
Canh V. Pham ◽  
Anh N. Su ◽  
Vaclav Snasel

Author(s):  
Eiji MIYANO ◽  
Toshiki SAITOH ◽  
Ryuhei UEHARA ◽  
Tsuyoshi YAGITA ◽  
Tom C. van der ZANDEN

2010 ◽  
Vol 69 (19) ◽  
pp. 1681-1702
Author(s):  
V. V. Lukin ◽  
S. K. Abramov ◽  
A. V. Popov ◽  
P. Ye. Eltsov ◽  
Benoit Vozel ◽  
...  

1979 ◽  
Vol 44 (2) ◽  
pp. 328-339
Author(s):  
Vladimír Herles

Contradictious results published by different authors about the dynamics of systems with random parameters have been examined. Statistical analysis of the simple 1st order system proves that the random parameter can cause a systematic difference in the dynamic behavior that cannot be (in general) described by the usual constant-parameter model with the additive noise at the output.


Author(s):  
Jochen Jungeilges ◽  
Elena Maklakova ◽  
Tatyana Perevalova

AbstractWe study the price dynamics generated by a stochastic version of a Day–Huang type asset market model with heterogenous, interacting market participants. To facilitate the analysis, we introduce a methodology that allows us to assess the consequences of changes in uncertainty on the dynamics of an asset price process close to stable equilibria. In particular, we focus on noise-induced transitions between bull and bear states of the market under additive as well as parametric noise. Our results are obtained by combining the stochastic sensitivity function (SSF) approach, a mixture of analytical and numerical techniques, due to Mil’shtein and Ryashko (1995) with concepts and techniques from the study of non-smooth 1D maps. We find that the stochastic sensitivity of the respective bull and bear equilibria in the presence of additive noise is higher than under parametric noise. Thus, recurrent transitions are likely to be observed already for relatively low intensities of additive noise.


2021 ◽  
pp. 1-13
Author(s):  
Haitao Liu ◽  
Yew-Soon Ong ◽  
Ziwei Yu ◽  
Jianfei Cai ◽  
Xiaobo Shen

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