coordinate descent algorithm
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Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 237
Author(s):  
Ionuț-Dorinel Fîciu ◽  
Cristian-Lucian Stanciu ◽  
Camelia Elisei-Iliescu ◽  
Cristian Anghel

The recently proposed tensor-based recursive least-squares dichotomous coordinate descent algorithm, namely RLS-DCD-T, was designed for the identification of multilinear forms. In this context, a high-dimensional system identification problem can be efficiently addressed (gaining in terms of both performance and complexity), based on tensor decomposition and modeling. In this paper, following the framework of the RLS-DCD-T, we propose a regularized version of this algorithm, where the regularization terms are incorporated within the cost functions. Furthermore, the optimal regularization parameters are derived, aiming to attenuate the effects of the system noise. Simulation results support the performance features of the proposed algorithm, especially in terms of its robustness in noisy environments.


2021 ◽  
Author(s):  
Huifeng Jiang ◽  
Xuemei Hu ◽  
Hong Jia

Abstract Predicting up and down trends for stock prices is an important puzzle in the financial field. Hu & Jiang (2021) proposed logistic regression with 6 technical indicators to predict up and down trends for Google's stock prices. In this paper we further propose the five penalized logistic regressions with 19 technical indicators: ridge (L2), lasso (L1), elastic net(EN), smoothly clipped absolute deviation (SCAD) and minimax concave penalty (MCP) to improve the prediction accuracy. Firstly, we combine the iterative weighted least square algorithm with the coordinate descent algorithm, and apply a training set to obtain parameter estimators and probability estimators. Then we adopt a test set to construct confusion matrices and receiver operating characteristic (ROC) curves, and apply them to assess their prediction performances. Finally we compare the proposed five prediction methods with logistic regression, support vector machine (SVM) and artificial neural network (ANN) , and found that the MCP penalized logistic regression performs the best. Therefore, we develop a new efficient prediction method to predict up and down trends for stock prices.


Axioms ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 331
Author(s):  
EunJi Lee ◽  
Jae-Hwan Jhong

We consider a function estimation method with change point detection using truncated power spline basis and elastic-net-type L1-norm penalty. The L1-norm penalty controls the jump detection and smoothness depending on the value of the parameter. In terms of the proposed estimators, we introduce two computational algorithms for the Lagrangian dual problem (coordinate descent algorithm) and constrained convex optimization problem (an algorithm based on quadratic programming). Subsequently, we investigate the relationship between the two algorithms and compare them. Using both simulation and real data analysis, numerical studies are conducted to validate the performance of the proposed method.


Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 2966
Author(s):  
Zhi Quan ◽  
Yingying Zhang ◽  
Jie Liu ◽  
Yao Wang

In this paper, we devise an efficient approach for estimating the direction of arrival (DoA). The proposed DoA estimation approach is based on minimum variance distortionless response (MVDR) criteria within a recursive least squares (RLS) framework. The dichotomous coordinate descent algorithm is used to modify the calculation of the output power spectrum, and a diagonal loading term is applied to improve the robustness of the DoA estimator. These modifications allow us to both reduce the computational complexity of the RLS DoA estimator and increase the estimation performance. A numerical comparison confirms that the proposed DoA estimator outperforms the conventional RLS DoA estimator in terms of the computational complexity and DoA estimation error. Finally, the proposed theoretical DoA estimator is implemented on a field-programmable gate array (FPGA) board to verify the feasibility of the method. The numerical results of a fixed-point implementation demonstrate that the performance of the proposed method is very close to that of its floating-point counterpart.


Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1473
Author(s):  
Yan Wang ◽  
Jiali Chen ◽  
Xuping Xie ◽  
Sen Yang ◽  
Wei Pang ◽  
...  

Support vector clustering (SVC) is a boundary-based algorithm, which has several advantages over other clustering methods, including identifying clusters of arbitrary shapes and numbers. Leveraged by the high generalization ability of the large margin distribution machine (LDM) and the optimal margin distribution clustering (ODMC), we propose a new clustering method: minimum distribution for support vector clustering (MDSVC), for improving the robustness of boundary point recognition, which characterizes the optimal hypersphere by the first-order and second-order statistics and tries to minimize the mean and variance simultaneously. In addition, we further prove, theoretically, that our algorithm can obtain better generalization performance. Some instructive insights for adjusting the number of support vector points are gained. For the optimization problem of MDSVC, we propose a double coordinate descent algorithm for small and medium samples. The experimental results on both artificial and real datasets indicate that our MDSVC has a significant improvement in generalization performance compared to SVC.


2021 ◽  
Vol 2078 (1) ◽  
pp. 012012
Author(s):  
Song Yao ◽  
Lipeng Cui ◽  
Sining Ma

Abstract In recent years, the sparse model is a research hotspot in the field of artificial intelligence. Since the Lasso model ignores the group structure among variables, and can only achieve the selection of scattered variables. Besides, Group Lasso can only select groups of variables. To address this problem, the Sparse Group Log Ridge model is proposed, which can select both groups of variables and variables in one group. Then the MM algorithm combined with the block coordinate descent algorithm can be used for solving. Finally, the advantages of the model in terms of variables selection and prediction are shown through the experiment.


2021 ◽  
Vol 11 (18) ◽  
pp. 8769
Author(s):  
Jun Long ◽  
Longzhi Sun ◽  
Liujie Hua ◽  
Zhan Yang

Cross-modal hashing technology is a key technology for real-time retrieval of large-scale multimedia data in real-world applications. Although the existing cross-modal hashing methods have achieved impressive accomplishment, there are still some limitations: (1) some cross-modal hashing methods do not make full consider the rich semantic information and noise information in labels, resulting in a large semantic gap, and (2) some cross-modal hashing methods adopt the relaxation-based or discrete cyclic coordinate descent algorithm to solve the discrete constraint problem, resulting in a large quantization error or time consumption. Therefore, in order to solve these limitations, in this paper, we propose a novel method, named Discrete Semantics-Guided Asymmetric Hashing (DSAH). Specifically, our proposed DSAH leverages both label information and similarity matrix to enhance the semantic information of the learned hash codes, and the ℓ2,1 norm is used to increase the sparsity of matrix to solve the problem of the inevitable noise and subjective factors in labels. Meanwhile, an asymmetric hash learning scheme is proposed to efficiently perform hash learning. In addition, a discrete optimization algorithm is proposed to fast solve the hash code directly and discretely. During the optimization process, the hash code learning and the hash function learning interact, i.e., the learned hash codes can guide the learning process of the hash function and the hash function can also guide the hash code generation simultaneously. Extensive experiments performed on two benchmark datasets highlight the superiority of DSAH over several state-of-the-art methods.


2021 ◽  
Author(s):  
Jose A Villegas ◽  
Tasneem M Vaid ◽  
Michael E Johnson ◽  
Terry W Moore

One of the principal difficulties in computational modeling of macromolecules is the vast conformational space that arises out of large numbers of atomic degrees of freedom. This problem is a familiar issue in the area of protein-protein docking, where models of protein complexes are generated from the monomeric subunits. Although restriction of molecular flexibility is a commonly used approximation that decreases the dimensionality of the problem, the seemingly endless number of possible ways two binding partners can interact generally necessitates the use of further approximations to explore the search space. Recently, growing interest in using computational tools to build predictive models of PROTAC-mediated complexes has led to the application of state-of-the-art protein-protein docking techniques to tackle this problem. Additionally, the atomic degrees of freedom introduced by flexibility of linkers used in the construction of PROTACs further expands the configurational search space, a problem that can be tackled with conformational sampling tools. However, repurposing existing tools to carry out protein-protein docking and linker conformer generation independently results in extensive sampling of structures incompatible with PROTAC-mediated complex formation. Here we show that it is possible to restrict the search to the space of protein-protein conformations that can be bridged by a PROTAC molecule with a given linker composition by using a cyclic coordinate descent algorithm to position PROTACs into complex-bound configurations. We use this methodology to construct a picture of the energy landscape of PROTAC-mediated interactions in a model test case, and show that the global minimum lies in the space of native-like conformations.


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