Results 31 to 40 of about 337,735 (278)
Sparse Matrix Methods in Optimization [PDF]
SIAM Journal on Scientific and Statistical Computing, 1984 Sparse matrix techniques for the solution of three subdivisions of optimization are surveyed. Newton type, sparse quasi-Newton type methods conjugate-gradient methods are considered for the unconstrained optimization. Solving the null-space equations and the range-space equations for the linearly constrained optimization, the emphasis is laid on the ...
Gill, Philip E. +3 more
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Complex & Intelligent Systems, 2023 Sparse multiobjective optimization problems are common in practical applications. Such problems are characterized by large-scale decision variables and sparse optimal solutions.
Jin Ren, Feiyue Qiu, Huizhen Hu
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, 2022 Bayesian optimization (BO) is a powerful approach to sample-efficient optimization of black-box objective functions. However, the application of BO to areas such as recommendation systems often requires taking the interpretability and simplicity of the configurations into consideration, a setting that has not been previously studied in the BO ...
Liu, Sulin +4 more
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K-Complex Detection Based on Synchrosqueezing Transform [PDF]
AUT Journal of Electrical Engineering, 2017 K-complex is an underlying pattern in the sleep EEG. Due to the role of sleep studies inneurophysiologic and cognitive disorders diagnosis, reliable methods for analysis and detection of this patternare of great importance.
Z. Ghanbari, M. H. Moradi
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Nearly optimal sparse fourier transform [PDF]
Proceedings of the forty-fourth annual ACM symposium on Theory of computing, 2012 28 pages, appearing at STOC ...
Hassanieh, Haitham +3 more
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Sparse optimal stochastic control [PDF]
Automatica, 2021 In this paper, we investigate a sparse optimal control of continuous-time stochastic systems. We adopt the dynamic programming approach and analyze the optimal control via the value function. Due to the non-smoothness of the $L^0$ cost functional, in general, the value function is not differentiable in the domain.
Ito, Kaito +2 more
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Nearly optimal sparse group testing [PDF]
2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2016 Group testing is the process of pooling arbitrary subsets from a set of $n$ items so as to identify, with a minimal number of tests, a "small" subset of $d$ defective items. In "classical" non-adaptive group testing, it is known that when $d$ is substantially smaller than $n$, $ (d\log(n))$ tests are both information-theoretically necessary and ...
Venkata Gandikota +3 more
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Information-theoretically optimal sparse PCA [PDF]
2014 IEEE International Symposium on Information Theory, 2014 Sparse Principal Component Analysis (PCA) is a dimensionality reduction technique wherein one seeks a low-rank representation of a data matrix with additional sparsity constraints on the obtained representation. We consider two probabilistic formulations of sparse PCA: a spiked Wigner and spiked Wishart (or spiked covariance) model.
Deshpande, Yash, Montanari, Andrea
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Genetic Algorithm for Sparse Optimization of Mills Cross Array Used in Underwater Acoustic Imaging
Journal of Marine Science and Engineering, 2022 Underwater acoustic imaging employs a special form of array which includes numerous transducer elements to achieve beamforming. Although a large-scale array can bring high imaging resolution, it will also cause difficulties in hardware complexity and ...
Duo Teng +4 more
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Compressive Sampling for Remote Control Systems [PDF]
, 2012 In remote control, efficient compression or representation of control signals is essential to send them through rate-limited channels. For this purpose, we propose an approach of sparse control signal representation using the compressive sampling ...
Hayashi, Kazunori +2 more
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