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Sparse Polynomial Optimization
2022The problem of minimizing a polynomial over a set of polynomial inequalities is an NP-hard non-convex problem. Thanks to powerful results from real algebraic geometry, one can convert this problem into a nested sequence of finite-dimensional convex problems. At each step of the associated hierarchy, one needs to solve a fixed size semidefinite program,
Magron, Victor, Wang, Jie
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Differentiable Sparse Optimal Control
IEEE Control Systems Letters, 2023This letter develops a framework for differentiating sparse optimal control inputs with respect to cost parameters. The difficulty lies in the non-smoothness induced by a sparsity-enhancing regularizer. To avoid this, we identify the optimal inputs as a unique zero point of a function using the proximal technique.
Ryotaro Shima +3 more
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Sparse Optimization for Motion Segmentation
2015In this paper, we propose a new framework for segmenting feature-based multiple moving objects with subspace models in affine views. Since the feature data is high-dimensional and complex in the real video sequences, most traditional approaches for motion segmentation use the conventional PCA to obtain a low-dimensional representation, while our ...
Yang, Michael Ying +2 more
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Sparse optimal discriminant clustering
Statistics and Computing, 2015zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wang, Yanhong, Fang, Yixin, Wang, Junhui
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Generalised Optimal Sparse Predictors
2017This chapter presents a new linear prediction framework for the HEVC standard, which generalises the directional and linear prediction methods, using optimal sparse predictors combined with geometric transformations. Ultimately, each angular mode of directional prediction can be regarded as a set of very simple linear predictors, a different one for ...
Luís Filipe Rosário Lucas +4 more
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Optimization for sparse acquisition
SEG Technical Program Expanded Abstracts 2015, 2015Acquisition design plays a significant role in seismic exploration and data processing. An optimized seismic acquisition design will require fewer resources and therefore, it can reduce the total cost of seismic exploration. Finding the optimal locations of sources and receivers in a seismic survey is a long-standing problem.
Mafijul Bhuiyan, Mauricio Sacchi
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Dose‐shaping using targeted sparse optimization
Medical Physics, 2013Purpose:Dose volume histograms (DVHs) are common tools in radiation therapy treatment planning to characterize plan quality. As statistical metrics, DVHs provide a compact summary of the underlying plan at the cost of losing spatial information: the same or similar dose‐volume histograms can arise from substantially different spatial dose maps. This is
George A, Sayre, Dan, Ruan
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Stochastic optimization of linear sparse arrays
IEEE Journal of Oceanic Engineering, 1999In conventional beamforming systems, the use of aperiodic arrays is a powerful way to obtain high resolution employing few elements and avoiding the presence of grating lobes. The optimized design of such arrays is a required task in order to control the side-lobe level and distribution.
TRUCCO, ANDREA, V. MURINO
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Sparse Learning with Stochastic Composite Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017In this paper, we study Stochastic Composite Optimization (SCO) for sparse learning that aims to learn a sparse solution from a composite function. Most of the recent SCO algorithms have already reached the optimal expected convergence rate O(1/λT), but they often fail to deliver sparse solutions at the end either due to the limited sparsity ...
Weizhong Zhang +7 more
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