444 results on '"Regular polygon"'
Search Results
2. New Method for Generating a Regular Polygon
- Author
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Lebamovski, Penio Dimitrov, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Yang, Xin-She, editor, Sherratt, R. Simon, editor, Dey, Nilanjan, editor, and Joshi, Amit, editor
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- 2023
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3. Regular Polygon Central Configuration of the Restricted 1 + N -Body Problem.
- Author
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Chen, Jian, Bi, Peng, and Yang, Mingfang
- Subjects
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POLYGONS , *CELESTIAL mechanics - Abstract
We consider the central configurations of the 1 + N -body problem, where N bodies are infinitesimal and the remaining one body is dominant. For regular polygon central configurations, we prove that the masses of all the infinitesimal bodies are equal when N is odd and the masses of the alternate infinitesimal bodies must be equal when N is even. Moreover, in the case of N being even, we present the relationship of the mass parameters between two consecutive infinitesimal bodies. [ABSTRACT FROM AUTHOR]
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- 2023
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4. Solving geometry problems by alternative methods in mathematics education.
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Aliyev, Samed J., Heydarova, Maftun N., and Aghazade, Shahin M.
- Subjects
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MATHEMATICS education , *PROBLEM solving , *CIRCLE , *GEOMETRY - Abstract
Solving geometry problems is both difficult and interesting. Difficult because there is no general algorithm to solve more or less non-trivial problems as every single problem requires individual and creative approach. At the same time, this is a very interesting activity, because for almost every problem there are plenty of ways to solve it. In this work, we present the method of auxiliary circle divided into equal parts. This method allows finding solution algorithm for some geometry problems which are hard to solve by the method of additional constructions. [ABSTRACT FROM AUTHOR]
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- 2023
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5. TWO NON-CONGRUENT REGULAR POLYGONS HAVING VERTICES AT THE SAME DISTANCES FROM THE POINT.
- Author
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Meskhishvili, Mamuka
- Subjects
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GEOMETRICAL constructions - Abstract
For the given regular plane polygon and an arbitrary point in the plane of the polygon, the distances from the point to the vertices of the polygon are defined. We proved that there is one more non-congruent regular polygon having the vertices at the same distances from the point. The sizes of both regular polygons are uniquely determined by these distances. In general case, geometrical construction of the second regular polygon is given. It is proved that there are two points in the plane, which separately have the same set of the distances to the vertices of two non-congruent regular polygons with a shared vertex. [ABSTRACT FROM AUTHOR]
- Published
- 2023
6. Synthesis of 1-DOF mechanisms for exact regular polygonal path generation based on non-circular gear transmissions.
- Author
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Castillo, Carlos, López-Martínez, Javier, García-Vallejo, Daniel, and Blanco-Claraco, José Luis
- Subjects
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ACCELERATION (Mechanics) , *GEARING machinery , *KINEMATICS , *POLYGONS , *VELOCITY , *ANGLES - Abstract
Design of one-degree-of-freedom (1-DOF) mechanisms is of paramount interest. This work deals with the generation of exact regular polygonal paths by using two particular 1-DOF mechanisms. The design of two-bar and three-bar mechanisms including non-circular gears is presented and evaluated. Differences in the kinematics of both mechanisms are discussed. The three-bar mechanism allows derivable velocity and acceleration curves during the whole trajectory, including the polygon vertices, a feature that cannot be achieved with the simpler two-bar mechanism. Hence, the three-bar mechanism makes it possible to generate perfect vertices using non-circular gears. To the best of the authors' knowledge, this mechanism is the first 1-DOF mechanism with articulated links and a non-circular gear transmission that can generate exact regular polygonal paths. The proposed mechanisms can also be applied to generate two straight lines with a given angle, which is another novel contribution of this work. A prototype of the three-bar mechanism has been developed for experimental validation. • A 1-DOF mechanism that can generate exact regular polygonal paths is presented. • Solutions with two- and three-bar mechanisms are analyzed. • The mechanism can also generate two exact straight lines with a given angle. • Non-circular gears are used to generate the required non-linear transmission. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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7. Regular Polygon Central Configuration of the Restricted 1 + N-Body Problem
- Author
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Jian Chen, Peng Bi, and Mingfang Yang
- Subjects
celestial mechanics ,central configuration ,restricted 1 + N-body problem ,regular polygon ,Elementary particle physics ,QC793-793.5 - Abstract
We consider the central configurations of the 1+N-body problem, where N bodies are infinitesimal and the remaining one body is dominant. For regular polygon central configurations, we prove that the masses of all the infinitesimal bodies are equal when N is odd and the masses of the alternate infinitesimal bodies must be equal when N is even. Moreover, in the case of N being even, we present the relationship of the mass parameters between two consecutive infinitesimal bodies.
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- 2023
- Full Text
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8. Free vibration and buckling of heavy column with regular polygon cross-section.
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Lee, Joon Kyu and Lee, Byoung Koo
- Abstract
This paper deals with the free vibration and buckling of heavy column, considering its own self-weight. The column has a regular polygonal cross-section with a constant area. The column is applied to an external axial load as well as the self-weight. The five end conditions of the column are considered. Based on equilibrium equations of the column element, differential equations governing the vibrational and buckled mode shapes of column are derived. In solution methods, differential equations are numerically integrated by the direct integration method and eigenvalues of the natural frequency, buckling load and self-weight buckling length are calculated by the determinant search method. The numerical results of this study were in good agreement with those of the reference. Parametric study of the end condition, side number and self-weight on the natural frequency and buckling load was carried out. [ABSTRACT FROM AUTHOR]
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- 2022
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9. Buckling optimization of axially functionally graded columns having constant volume.
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Lee, Joon Kyu and Lee, Byoung Koo
- Subjects
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EULER-Bernoulli beam theory , *YOUNG'S modulus , *FINITE element method , *MODE shapes , *SEARCH algorithms - Abstract
In this article, the buckling optimization of axially functionally graded (AFG) columns to maximize the buckling capacity is studied. Consideration is given to an AFG column having a tapered regular polygon cross section and variable material properties. The governing differential equation is derived based on Euler–Bernoulli beam theory with the relevant boundary conditions and is solved using the direct integration method combined with a determinant search algorithm. The computed buckling loads are compared with those presented in the literature and obtained from finite element analysis. Numerical examples for buckling load and buckled mode shape are given to highlight the effect of parameters related to the Young's modulus, cross-sectional shape, tapering and column volume. In particular, the geometry and material parameters that provide buckling optimization at constant volume of the column are analysed. [ABSTRACT FROM AUTHOR]
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- 2022
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10. Constructive Geometric Generating of Concave Pyramids of Fourth Sort.
- Author
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Mišić, Slobodan Ž. and Backo, Marija V.
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PYRAMIDS ,UNIT cell ,DISTRIBUTION (Probability theory) ,TRIANGLES ,POLYHEDRA - Abstract
Copyright of FME Transactions is the property of University of Belgrade, Faculty of Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2021
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11. Elliptic Localization of a Moving Object by Transmitter at Unknown Position and Velocity: A Semidefinite Relaxation Approach
- Author
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Dominic K. C. Ho, Gang Wang, and Ruichao Zheng
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Mean squared error ,Computer Networks and Communications ,Computer science ,Transmitter ,Regular polygon ,Upper and lower bounds ,Local convergence ,symbols.namesake ,Gaussian noise ,Position (vector) ,symbols ,Relaxation (approximation) ,Electrical and Electronic Engineering ,Algorithm ,Software ,Computer Science::Information Theory - Abstract
This paper investigates the elliptic localization for moving object problem from time delay (TD) and Doppler frequency shift (DFS) measurements, where the transmitter position and velocity are unknown. The transmitter is not perfectly time syncronized such that unknown offsets exist in the TD and DFS measurements. We propose to jointly estimate the object and transmitter positions and velocities and the offsets. Using the TD and DFS measurements from both the indirect and direct paths between the transmitter and the receivers, we formulate a non-convex weighted least squares (WLS) problem. Local convergence may occur when solving the non-convex WLS problem, implying that good estimate is not guaranteed. Thus, we relax the non-convex WLS problem into a convex semidefinite program by applying semidefinite relaxation (SDR). Moreover, we theoretically show that the performance can be improved by using multiple transmitters as compared to that using single transmitter, although more unknown parameters are introduced. We then extend the proposed SDR method to handle the multiple transmitters case. Finally, the mean square error analysis is provided to show that the proposed WLS method reaches the Cramer-Rao lower bound accuracy under small Gaussian noise condition. Simulation results validate the theoretical analysis and show the superior performance over the existing methods.
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- 2023
12. On inverse construction of isoptics and isochordal-viewed curves.
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Rochera, David and Bartoň, Michael
- Subjects
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INVERSE problems , *POLYGONS , *LOCUS (Mathematics) - Abstract
Given a regular closed curve α in the plane, a ϕ -isoptic of α is a locus of points from which pairs of tangent lines to α span a fixed angle ϕ. If, in addition, the chord that connects the two points delimiting the visibility angle is of constant length ℓ , then α is said to be (ϕ , ℓ) -isochordal viewed. Some properties of these curves have been studied, yet their full classification is not known. We approach the problem in an inverse manner, namely that we consider a ϕ -isoptic curve c as an input and construct a curve whose ϕ -isoptic is c. We provide thus a sufficient condition that constitutes a partial solution to the inverse isoptic problem. In the process, we also study a relation of isoptics to multihedgehogs. Moreover, we formulate conditions on the behavior of the visibility lines so as their envelope is a (ϕ , ℓ) -isochordal-viewed curve with a prescribed ϕ -isoptic c. Our results are constructive and offer a tool to easily generate this type of curves. In particular, we show examples of (ϕ , ℓ) -isochordal-viewed curves whose ϕ -isoptic is not circular. Finally, we prove that these curves allow the motion of a regular polygon whose vertices lie along the (ϕ , ℓ) -isochordal-viewed curve. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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13. Modified Dai-Yuan iterative scheme for nonlinear systems and its application
- Author
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Mohammed Yusuf Waziri, Aliyu Mohammed Awwal, Abubakar Sani Halilu, and Kabiru Ahmed
- Subjects
Nonlinear system ,Signal processing ,Control and Optimization ,Algebra and Number Theory ,Monotone polygon ,Iterative method ,Computer science ,Applied Mathematics ,Convergence (routing) ,Projection method ,Regular polygon ,Applied mathematics ,Image (mathematics) - Abstract
By exploiting the idea employed in the spectral Dai-Yuan method by Xue et al. [IEICE Trans. Inf. Syst. 101 (12)2984-2990 (2018)] and the approach applied in the modified Hager-Zhang scheme for nonsmooth optimization [PLos ONE 11(10): e0164289 (2016)], we develop a Dai-Yuan type iterative scheme for convex constrained nonlinear monotone system. The scheme's algorithm is obtained by combining its search direction with the projection method [Kluwer Academic Publishers, pp. 355-369(1998)]. One of the new scheme's attribute is that it is derivative-free, which makes it ideal for solving non-smooth problems. Furthermore, we demonstrate the method's application in image de-blurring problems by comparing its performance with a recent effective method. By employing mild assumptions, global convergence of the scheme is determined and results of some numerical experiments show the method to be favorable compared to some recent iterative methods.
- Published
- 2023
14. Incremental subgradient algorithms with dynamic step sizes for separable convex optimizations
- Author
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Xiangmei Wang and Dan Yang
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Computer science ,Component (UML) ,General Mathematics ,Convergence (routing) ,Regular polygon ,General Engineering ,Convex function ,Assignment problem ,Subgradient method ,Algorithm ,Separable space - Abstract
We consider the incremental subgradient algorithm employing dynamic step sizes for minimizing the sum of a large number of component convex functions. The dynamic step size rule was firstly introduced by Goffin and Kiwiel [Math. Program., 1999, 85(1): 207-211] for the subgradient algorithm, soon later, for the incremental subgradient algorithm by Nedic and Bertsekas in [SIAM J. Optim., 2001, 12(1): 109-138]. It was observed experimentally that the incremental approach has been very successful in solving large separable optimizations, and that the dynamic step sizes generally have better computational performance than others in the literature. In the present paper, we propose two modified dynamic step size rules for the incremental subgradient algorithm and analyse the convergence properties of them. At last, the assignment problem is considered and the incremental subgradient algorithms employing different kinds of dynamic step sizes are applied to solve the problem. The computational experiments show that the two modified ones converges dramatically faster and stabler than the corresponding one in [SIAM J. Optim., 2001, 12(1): 109-138]. Particularly, for solving large separable convex optimizations, we strongly recommend the second one (see Algorithm 3.3 in the paper) since it has interesting computational performance and is the simplest one.
- Published
- 2022
15. Penetration Depth Between Two Convex Polyhedra: An Efficient Stochastic Global Optimization Approach
- Author
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Mark A. Abramson, Gavin W. Smith, and Griffin D. Kent
- Subjects
Mathematical optimization ,Optimization problem ,Discretization ,Computer science ,Regular polygon ,Polytope ,Computer Graphics and Computer-Aided Design ,Computer graphics ,Polyhedron ,Signal Processing ,Computer Vision and Pattern Recognition ,Focus (optics) ,Global optimization ,Software - Abstract
During the detailed design phase of an aerospace program, one of the most important consistency checks is to ensure that no two distinct objects occupy the same physical space. Since exact geometrical modeling is usually intractable, geometry models are discretized, which often introduces small interferences not present in the fully detailed model. In this paper, we focus on computing the depth of the interference, so that these false positive interferences can be removed, and attention can be properly focused on the actual design. Specifically, we focus on efficiently computing the penetration depth between two polyhedra, which is a well-studied problem in the computer graphics community. We formulate the problem as a constrained five-variable global optimization problem, and then derive an equivalent unconstrained, two-variable nonsmooth problem. To solve the optimization problem, we apply a popular stochastic multistart optimization algorithm in a novel way, which exploits the advantages of each problem formulation simultaneously. Numerical results for the algorithm, applied to 14 randomly generated pairs of penetrating polytopes, illustrate both the effectiveness and efficiency of the method.
- Published
- 2022
16. Top-k Partial Label Machine
- Author
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Dong Yuan, Xiuwen Gong, and Wei Bao
- Subjects
Optimization algorithm ,Linear programming ,Computer Networks and Communications ,Computer science ,business.industry ,Regular polygon ,Pattern recognition ,Computer Science Applications ,Dual (category theory) ,Set (abstract data type) ,Artificial Intelligence ,Partial loss ,Hinge loss ,Artificial intelligence ,Noise (video) ,business ,Software - Abstract
To deal with ambiguities in partial label learning (PLL), the existing PLL methods implement disambiguations, by either identifying the ground-truth label or averaging the candidate labels. However, these methods can be easily misled by the false-positive labels in the candidate label set. We find that these ambiguities often originate from the noise caused by highly correlated or overlapping candidate labels, which leads to the difficulty in identifying the ground-truth label on the first attempt. To give the trained models more tolerance, we first propose the top-k partial loss and convex top-k partial hinge loss. Based on the losses, we present a novel top-k partial label machine (TPLM) for partial label classification. An efficient optimization algorithm is proposed based on accelerated proximal stochastic dual coordinate ascent (Prox-SDCA) and linear programming (LP). Moreover, we present a theoretical analysis of the generalization error for TPLM. Comprehensive experiments on both controlled UCI datasets and real-world partial label datasets demonstrate that the proposed method is superior to the state-of-the-art approaches.
- Published
- 2022
17. On the Δ-interval and the Δ-convexity numbers of graphs and graph products
- Author
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Bijo S. Anand, Mitre Costa Dourado, Prasanth G. Narasimha-Shenoi, and Sabeer Sain Ramla
- Subjects
Combinatorics ,Cardinality ,Chordal graph ,Applied Mathematics ,Regular polygon ,Block (permutation group theory) ,Discrete Mathematics and Combinatorics ,Interval (graph theory) ,Function (mathematics) ,Lexicographical order ,Convexity ,Mathematics - Abstract
Given a graph G and a set S ⊆ V ( G ) , the Δ -interval of S , [ S ] Δ , is the set formed by the vertices of S and every w ∈ V ( G ) forming a triangle with two vertices of S . If [ S ] Δ = S , then S is Δ -convex of G ; if [ S ] Δ = V ( G ) , then S is a Δ -interval set of G . The Δ -interval number of G is the minimum cardinality of a Δ -interval set and the Δ -convexity number of G is the maximum cardinality of a proper Δ -convex subset of V ( G ) . In this work, we show that the problem of computing the Δ -convexity number is W[1]-hard and NP-hard to approximate within a factor O ( n 1 − ɛ ) for any constant ɛ > 0 even for graphs with diameter 2 and that the problem of computing the Δ -interval number is NP-complete for general graphs. For the positive side, we present characterizations that lead to polynomial-time algorithms for computing the Δ -convexity number of chordal graphs and for computing the Δ -interval number of block graphs. We also present results on the Δ -hull, Δ -interval and Δ -convexity numbers concerning the three standard graph products, namely, the Cartesian, the strong and the lexicographic products, in function of these and well-studied parameters of the operands.
- Published
- 2022
18. Convex preferences: An abstract approach
- Author
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Marta Cardin
- Subjects
TheoryofComputation_MISCELLANEOUS ,Settore SECS-S/06 - Metodi mat. dell'economia e Scienze Attuariali e Finanziarie ,Logic ,MathematicsofComputing_NUMERICALANALYSIS ,MathematicsofComputing_GENERAL ,Regular polygon ,Convex preferences ,Convexity ,Convex structure ,Combinatorics ,Areas of mathematics ,Artificial Intelligence ,TheoryofComputation_ANALYSISOFALGORITHMSANDPROBLEMCOMPLEXITY ,Convex space Convexity algebra Convex preference Compatible preference Aggregation of orderings ,Mathematics - Abstract
The notion of abstract convex structure generalizes the standard notion of convexity in linear spaces. We consider abstract convex structures that are combinatorial objects studied in various areas of mathematics and convex algebras as introduced in [8] and we study a general definition of convex preferences. Relations defined by aggregation of orderings are considered.
- Published
- 2022
19. Accelerated Log-Regularized Convolutional Transform Learning and Its Convergence Guarantee
- Author
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Haoli Zhao, Shengli Xie, Zuyuan Yang, Yongcheng Guo, and Zhenni Li
- Subjects
Computer science ,Open problem ,Regular polygon ,Extrapolation ,02 engineering and technology ,Function (mathematics) ,Convolutional neural network ,Computer Science Applications ,Human-Computer Interaction ,CTL ,Control and Systems Engineering ,020204 information systems ,Convergence (routing) ,0202 electrical engineering, electronic engineering, information engineering ,Unsupervised learning ,020201 artificial intelligence & image processing ,Electrical and Electronic Engineering ,Algorithm ,Software ,Information Systems - Abstract
Convolutional transform learning (CTL), learning filters by minimizing the data fidelity loss function in an unsupervised way, is becoming very pervasive, resulting from keeping the best of both worlds: the benefit of unsupervised learning and the success of the convolutional neural network. There have been growing interests in developing efficient CTL algorithms. However, developing a convergent and accelerated CTL algorithm with accurate representations simultaneously with proper sparsity is an open problem. This article presents a new CTL framework with a log regularizer that can not only obtain accurate representations but also yield strong sparsity. To efficiently address our nonconvex composite optimization, we propose to employ the proximal difference of the convex algorithm (PDCA) which relies on decomposing the nonconvex regularizer into the difference of two convex parts and then optimizes the convex subproblems. Furthermore, we introduce the extrapolation technology to accelerate the algorithm, leading to a fast and efficient CTL algorithm. In particular, we provide a rigorous convergence analysis for the proposed algorithm under the accelerated PDCA. The experimental results demonstrate that the proposed algorithm can converge more stably to desirable solutions with lower approximation error and simultaneously with stronger sparsity and, thus, learn filters efficiently. Meanwhile, the convergence speed is faster than the existing CTL algorithms.
- Published
- 2022
20. A note on the convexity number of the complementary prisms of trees
- Author
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P K Neethu and S V Ullas Chandran
- Subjects
Combinatorics ,Disjoint union ,Cardinality ,Applied Mathematics ,Shortest path problem ,Regular polygon ,Convex set ,Discrete Mathematics and Combinatorics ,Tree (graph theory) ,Convexity ,Complement (set theory) ,Mathematics - Abstract
A set of vertices S of a graph G is a (geodesically) convex set, if S contains all the vertices belonging to any shortest path connecting two vertices of S . The cardinality of a maximum proper convex set of G is called the convexity number, con ( G ) , of G . The complementary prism G G ¯ of G is obtained from the disjoint union of G and its complement G ¯ by adding the edges of a perfect matching between them. In this work, we examine the convex sets of the complementary prism of a tree and derive formulas for the convexity numbers of the complementary prisms of all trees.
- Published
- 2022
21. Support Vector Machine Classifier via Soft-Margin Loss
- Author
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Yuan-Hai Shao, Naihua Xiu, Shenglong Zhou, Ce Zhang, and Huajun Wang
- Subjects
Ideal (set theory) ,business.industry ,Computer science ,Applied Mathematics ,Working set ,Regular polygon ,Optimality theory ,Soft margin ,Support vector machine ,Computational Theory and Mathematics ,Artificial Intelligence ,Robustness (computer science) ,Limit point ,Computer Vision and Pattern Recognition ,Artificial intelligence ,business ,Algorithm ,Software - Abstract
Support vector machines (SVM) have drawn wide attention for the last two decades due to its extensive applications, so a vast body of work has developed optimization algorithms to solve SVM with various soft-margin losses. To distinguish all, in this paper, we aim at solving an ideal soft-margin loss SVM: L0/1 soft-margin loss SVM (dubbed as L0/1-SVM). Many of the existing (non)convex soft-margin losses can be viewed as one of the surrogates of the L0/1 soft-margin loss. Despite its discrete nature, we manage to establish the optimality theory for the L0/1-SVM including the existence of the optimal solutions, the relationship between them and P-stationary points. These not only enable us to deliver a rigorous definition of L0/1 support vectors but also allow us to define a working set. Integrating such a working set, a fast alternating direction method of multipliers is then proposed with its limit point being a locally optimal solution to the L0/1-SVM. Finally, numerical experiments demonstrate that our proposed method outperforms some leading classification solvers from SVM communities, in terms of faster computational speed and a fewer number of support vectors. The bigger the data size is, the more evident its advantage appears.
- Published
- 2022
22. Universal Prediction Band via Semi-Definite Programming
- Author
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Tengyuan Liang
- Subjects
FOS: Computer and information sciences ,Semidefinite programming ,Statistics and Probability ,Heteroscedasticity ,Computer Science - Machine Learning ,Econometrics (econ.EM) ,Nonparametric statistics ,Explained sum of squares ,Regular polygon ,Machine Learning (stat.ML) ,Mathematics - Statistics Theory ,Statistics Theory (math.ST) ,Variance (accounting) ,Machine Learning (cs.LG) ,FOS: Economics and business ,Optimization and Control (math.OC) ,Statistics - Machine Learning ,FOS: Mathematics ,Applied mathematics ,Uncertainty quantification ,Statistics, Probability and Uncertainty ,Mathematics - Optimization and Control ,Mathematics ,Interpolation ,Economics - Econometrics - Abstract
We propose a computationally efficient method to construct nonparametric, heteroscedastic prediction bands for uncertainty quantification, with or without any user-specified predictive model. Our approach provides an alternative to the now-standard conformal prediction for uncertainty quantification, with novel theoretical insights and computational advantages. The data-adaptive prediction band is universally applicable with minimal distributional assumptions, has strong non-asymptotic coverage properties, and is easy to implement using standard convex programs. Our approach can be viewed as a novel variance interpolation with confidence and further leverages techniques from semi-definite programming and sum-of-squares optimization. Theoretical and numerical performances for the proposed approach for uncertainty quantification are analyzed., 21 pages, 4 figures
- Published
- 2022
23. Generalized Nonconvex Approach for Low-Tubal-Rank Tensor Recovery
- Author
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Tingwen Huang, Xinling Liu, Feng Zhang, Jianwen Huang, Hailin Wang, and Jianjun Wang
- Subjects
Rank (linear algebra) ,Computer Networks and Communications ,Rank minimization ,Matrix norm ,Regular polygon ,Mathematical proof ,Computer Science Applications ,Critical point (set theory) ,Artificial Intelligence ,Tensor (intrinsic definition) ,Applied mathematics ,Minification ,Software ,Mathematics - Abstract
The tensor-tensor product-induced tensor nuclear norm (t-TNN) (Lu et al., 2020) minimization for low-tubal-rank tensor recovery attracts broad attention recently. However, minimizing the t-TNN faces some drawbacks. For example, the obtained solution could be suboptimal to the original problem due to its loose approximation. In this article, we extract a unified nonconvex surrogate of the tensor tubal rank as a tighter regularizer, which involves many popular nonconvex penalty functions. An iterative reweighted t-TNN algorithm is proposed to solve the resulting generalized nonconvex tubal rank minimization for tensor recovery. It converges to a critical point globally with rigorous proofs based on the Kurdyka-Łojasiwicz property. Furthermore, we provide the theoretical guarantees for exact and robust recovery by developing the tensor null space property. Extensive experiments demonstrate that our approach markedly enhances recovery performance compared with several state-of-the-art convex and nonconvex methods.
- Published
- 2022
24. Toward a Convex Design Framework for Online Active Fault Diagnosis of LPV Systems
- Author
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Feng Xu, Xueqian Wang, Sorin Olaru, Junbo Tan, Laboratoire des signaux et systèmes (L2S), and Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)
- Subjects
Design framework ,020301 aerospace & aeronautics ,0209 industrial biotechnology ,Sequence ,Computer science ,Regular polygon ,Characteristic equation ,02 engineering and technology ,Active fault ,[SPI.AUTO]Engineering Sciences [physics]/Automatic ,Computer Science Applications ,Constraint (information theory) ,020901 industrial engineering & automation ,Fractional programming ,0203 mechanical engineering ,Control and Systems Engineering ,Control theory ,Convex optimization ,Electrical and Electronic Engineering ,ComputingMilieux_MISCELLANEOUS - Abstract
This paper focuses on the design of on-line optimal input sequence for robust active fault diagnosis (AFD) of discrete-time linear parameter varying (LPV) systems using set-theoretic methods. Instead of the traditional set-separation constraint conditions leading to the design of off-line input sequence, the proposed approach focuses on on-line (re)shaping of the input sequence based on the real-time information of the output to discriminate system modes at each time instant such that the conservatism of robust AFD has the potential to be further reduced. The criterion on the design of optimal input is characterized based on a non-convex fractional programming problem at each time instant, which is shown to be efficiently solved within a convex optimization framework. Aside this main contribution, by exploiting Lagrange duality, the optimal input is explicitly obtained by solving a characteristic equation. At the end, a physical circuit model is provided to illustrate the effectiveness of the proposed method.
- Published
- 2022
25. On the Analysis of Inexact Augmented Lagrangian Schemes for Misspecified Conic Convex Programs
- Author
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Uday V. Shanbhag, Hesam Ahmadi, and Necdet Serhat Aybat
- Subjects
0209 industrial biotechnology ,020901 industrial engineering & automation ,Control and Systems Engineering ,Augmented Lagrangian method ,Conic section ,Regular polygon ,Applied mathematics ,02 engineering and technology ,Electrical and Electronic Engineering ,Computer Science Applications ,Mathematics - Published
- 2022
26. Private Empirical Risk Minimization With Analytic Gaussian Mechanism for Healthcare System
- Author
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Jiahao Ding, Haixia Zhang, Miao Pan, Sai Mounika Errapotu, Yuanxiong Guo, and Dongfeng Yuan
- Subjects
Information privacy ,Mathematical optimization ,Information Systems and Management ,Computer science ,Gaussian ,Regular polygon ,020207 software engineering ,02 engineering and technology ,symbols.namesake ,0202 electrical engineering, electronic engineering, information engineering ,symbols ,Differential privacy ,Empirical risk minimization ,Convex function ,Classifier (UML) ,Information Systems ,Healthcare system - Abstract
With the wide range application of machine learning in healthcare for helping humans drive crucial decisions, data privacy becomes an inevitable concern due to the utilization of sensitive data such as patients records and registers of a company. Thus, constructing a privacy preserving machine learning model while still maintaining high accuracy becomes a challenging problem. In this paper, we propose two differentially private algorithms, i.e., Output Perturbation with aGM (OPERA) and Gradient Perturbation with aGM (GRPUA) for empirical risk minimization, a useful method to obtain a globally optimal classifier, by leveraging the analytic Gaussian mechanism (aGM) to achieve privacy preservation of sensitive medical data in a healthcare system. We theoretically analyze and prove utility upper bounds of proposed algorithms and compare them with prior algorithms in the literature. The analyses show that in the high privacy regime, our proposed algorithms can achieve a tighter utility bound for both settings: strongly convex and non-strongly convex loss functions. Besides, we evaluate the proposed private algorithms on three benchmark datasets, i.e., Adult, BANK and IPUMS-BR. The simulation results demonstrate that our approaches can achieve higher accuracy and lower objective values compared with existing ones in all three datasets while providing differential privacy guarantees.
- Published
- 2022
27. The Robust Minkowski–Lyapunov Equation
- Author
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Saša V. Raković
- Subjects
Mathematical analysis ,Regular polygon ,Characterization (mathematics) ,Space (mathematics) ,Computer Science Applications ,symbols.namesake ,Control and Systems Engineering ,Minkowski space ,symbols ,Fundamental solution ,Lyapunov equation ,Uniqueness ,Electrical and Electronic Engineering ,Interior point method ,Mathematics - Abstract
The Lyapunov equation for polytopic linear inclusions over the space of Minkowski functions of nonempty compact and convex sets that contain the origin as an interior point is studied. In particular, necessary and sufficient conditions for the characterization, existence and uniqueness of its fundamental solution are derived.
- Published
- 2022
28. On Boundedness of Maximal Operators Associated with Hypersurfaces
- Author
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S E Usmanov and I A Ikromov
- Subjects
Statistics and Probability ,Pure mathematics ,Mathematics::Algebraic Geometry ,Hypersurface ,Mathematics::Complex Variables ,Applied Mathematics ,General Mathematics ,Mathematics::Classical Analysis and ODEs ,Regular polygon ,Mathematics::Differential Geometry ,General Medicine ,Value (mathematics) ,Mathematics - Abstract
In this paper, we obtain the criterion of boundedness of maximal operators associated with smooth hypersurfaces. Also we compute the exact value of the boundedness index of such operators associated with arbitrary convex analytic hypersurfaces in the case where the height of a hypersurface in the sense of A. N. Varchenko is greater than 2. Moreover, we obtain the exact value of the boundedness index for degenerated smooth hypersurfaces, i.e., for hypersurfaces satisfying conditions of the classical Hartman-Nirenberg theorem. The obtained results justify the Stein-Iosevich-Sawyer hypothesis for arbitrary convex analytic hypersurfaces as well as for smooth degenerated hypersurfaces. Also we discuss some related problems of the theory of oscillatory integrals.
- Published
- 2022
29. Distributed Adaptive Subgradient Algorithms for Online Learning Over Time-Varying Networks
- Author
-
Quanbo Ge, Junlong Zhu, Mingchuan Zhang, Bowei Hao, Qingtao Wu, and Ruijuan Zheng
- Subjects
Computer science ,Generalization ,Online learning ,Regular polygon ,Regret ,Computer Science Applications ,Human-Computer Interaction ,Control and Systems Engineering ,Bounded function ,Electrical and Electronic Engineering ,Performance improvement ,Clipping (computer graphics) ,Subgradient method ,Algorithm ,Software - Abstract
Adaptive gradient algorithms have recently become extremely popular because they have been applied successfully in training deep neural networks, such as Adam, AMSGrad, and AdaBound. Despite their success, however, the distributed variant of the adaptive method, which is expected to possess a rapid training speed at the beginning and a good generalization capacity at the end, is rarely studied. To fill the gap, a distributed adaptive subgradient algorithm is presented, called D-AdaBound, where the learning rates are dynamically bounded by clipping the learning rates. Moreover, we obtain the regret bound of D-AdaBound, in which the objective functions are convex. Finally, we confirm the effectiveness of D-AdaBound by simulation experiments on different datasets. The results show the performance improvement of D-AdaBound relative to existing distributed online learning algorithms.
- Published
- 2022
30. Smart meter data classification using optimized random forest algorithm
- Author
-
Alireza Zakariazadeh
- Subjects
Smart meter ,business.industry ,Computer science ,Applied Mathematics ,Data classification ,Regular polygon ,computer.software_genre ,Computer Science Applications ,Random forest ,Electricity ,Control and Systems Engineering ,Classifier (linguistics) ,Cluster Analysis ,Classification methods ,Data mining ,Electrical and Electronic Engineering ,Cluster analysis ,business ,Instrumentation ,computer ,Algorithms - Abstract
Implementing a proper clustering algorithm and a high accuracy classifier for applying on electricity smart meter data is the first stage in analyzing and managing electricity consumption. In this paper, Random Forest (RF) classifier optimized by Artificial Bee Colony (ABC) which is called Artificial Bee Colony-based Random Forest (ABC-RF) is proposed. Also, in order to determine the representative load curves, the Convex Clustering (CC) is used. The solution paths generated by convex clustering show relationships among clusters that were hidden by static methods such as k-means clustering. To validate the proposed method, a case study that includes a real dataset of residential smart meters is implemented. The results evidence that the proposed ABC-RF method provides a higher accuracy if compared to other classification methods.
- Published
- 2022
31. Yet Another Computation-Oriented Necessary and Sufficient Condition for Stabilizability of Switched Linear Systems
- Author
-
Mirko Fiacchini, GIPSA - Modelling and Optimal Decision for Uncertain Systems (GIPSA-MODUS), GIPSA Pôle Automatique et Diagnostic (GIPSA-PAD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), and ANR-18-CE40-0010,HANDY,Systèmes Dynamiques Hybrides et en Réseau(2018)
- Subjects
Switched linear systems ,020301 aerospace & aeronautics ,0209 industrial biotechnology ,convex analysis ,Computation ,[MATH.MATH-DS]Mathematics [math]/Dynamical Systems [math.DS] ,Linear system ,Mathematics::Optimization and Control ,Regular polygon ,stabilizability ,02 engineering and technology ,Space (mathematics) ,[SPI.AUTO]Engineering Sciences [physics]/Automatic ,Computer Science Applications ,Exponential convergence rate ,020901 industrial engineering & automation ,0203 mechanical engineering ,Cover (topology) ,Computer Science::Systems and Control ,Control and Systems Engineering ,Conic section ,Applied mathematics ,Partition (number theory) ,Electrical and Electronic Engineering ,Mathematics - Abstract
International audience; This paper presents a computational method to test the stabilizability of discrete-time switched linear systems. The existence of a conic cover of the space on whose elements a convex condition holds is proved to be necessary and sufficient for stabilizability. An algorithm for computing a conic partition that satisfies the new necessary and sufficient condition is given. The algorithm, that allows also to determine bounds on the exponential convergence rate, is proved to overcome the conservatism of conditions equivalent to periodic stabilizability and is applied to a four dimensional system.
- Published
- 2022
32. Distributed Nonconvex Event-Triggered Optimization Over Time-Varying Directed Networks
- Author
-
Wei Du, Yang Tang, Chen Liang, Zi wei Dong, Shuai Mao, and Yu-Chu Tian
- Subjects
Mathematical optimization ,Optimization problem ,Computer science ,Regular polygon ,Process (computing) ,Mode (statistics) ,Computer Science Applications ,Transmission (telecommunications) ,Rate of convergence ,Control and Systems Engineering ,Convergence (routing) ,Electrical and Electronic Engineering ,Information Systems ,Data transmission - Abstract
Many problems in industrial smart manufacturing, such as process operational optimization and decision-making, can be regarded as distributed non-convex optimization problems, whose goal is to utilize distributed nodes to cooperatively search for the minimal value of the global objective function. Considering data transmission mode, transmission condition and communication waste in industrial applications, it is meaningful to study the distributed non-convex optimization problem with an event-triggered strategy over time-varying directed networks. To solve such a problem, a distributed non-convex event-triggered algorithm is proposed in this paper. Under some assumptions on local objective functions, gradients and step-sizes, the convergence of the proposed event-triggered algorithm to the local minimum is proved. Moreover, it is obtained that the proposed distributed event-triggered algorithm has a convergence rate of O(1/ln(t)). Finally, two examples in industrial systems are provided to validate the effectiveness of the proposed algorithm.
- Published
- 2022
33. An exact algorithm for two-dimensional vector packing problem with volumetric weight and general costs
- Author
-
Qian Hu, Ting Wang, and Andrew Lim
- Subjects
Work (thermodynamics) ,Mathematical optimization ,Information Systems and Management ,General Computer Science ,Computer science ,Regular polygon ,Function (mathematics) ,Management Science and Operations Research ,Industrial and Manufacturing Engineering ,Piecewise linear function ,Set (abstract data type) ,Exact algorithm ,Bounding overwatch ,Modeling and Simulation ,Column generation - Abstract
The volumetric weight of a package has become an essential factor in calculating the delivery cost of shipments in the international logistics market. In this work, we extend the two-dimensional vector packing problem by considering a more realistic cost structure, which is a general function of volumetric weight. The problem is to pack a set of different items into some identical bins without violating weight limits and volume capacities so that the total delivery cost is minimized. We develop an exact approach based on a branch-and-price algorithm and subset-row inequalities for the problem. To efficiently solve the pricing problem in column generation, a label-setting algorithm with an effective label dominance rule and a bounding procedure is presented. A stronger label dominance rule is derived for the case where the cost function is convex. The computational results show that the exact method is effective in solving the various test instances of the problem. If the volumetric weight is not considered, the exact method can be adapted to solve the two-dimensional vector packing problem with piecewise linear cost function and outperformed the existing exact algorithm by computing 27 optimal solutions for previously open instances.
- Published
- 2022
34. A Convex Neural Network Solver for DCOPF With Generalization Guarantees
- Author
-
Baosen Zhang, Yize Chen, and Ling Zhang
- Subjects
Mathematical optimization ,Control and Optimization ,Karush–Kuhn–Tucker conditions ,Artificial neural network ,Computer Networks and Communications ,Generalization ,Computer science ,Regular polygon ,Systems and Control (eess.SY) ,Solver ,Electrical Engineering and Systems Science - Systems and Control ,Convexity ,Reduction (complexity) ,Electric power system ,Control and Systems Engineering ,Signal Processing ,FOS: Electrical engineering, electronic engineering, information engineering - Abstract
The DC optimal power flow (DCOPF) problem is a fundamental problem in power systems operations and planning. With high penetration of uncertain renewable resources in power systems, DCOPF needs to be solved repeatedly for a large amount of scenarios, which can be computationally challenging. As an alternative to iterative solvers, neural networks are often trained and used to solve DCOPF. These approaches can offer orders of magnitude reduction in computational time, but they cannot guarantee generalization, and small training error does not imply small testing errors. In this work, we propose a novel algorithm for solving DCOPF that guarantees the generalization performance. First, by utilizing the convexity of DCOPF problem, we train an input convex neural network. Second, we construct the training loss based on KKT optimality conditions. By combining these two techniques, the trained model has provable generalization properties, where small training error implies small testing errors. In experiments, our algorithm improves the optimality ratio of the solutions by a factor of five in comparison to end-to-end models.
- Published
- 2022
35. A categorical isomorphism between injective balanced L-S0-convex spaces and fuzzy frames
- Author
-
Changchun Xia
- Subjects
Combinatorics ,Artificial Intelligence ,Logic ,Frame (networking) ,Convex set ,Regular polygon ,Point (geometry) ,Isomorphism ,Categorical variable ,Fuzzy logic ,Injective function ,Mathematics - Abstract
The main purpose of this paper is to show that injective balanced L- S 0 -convex spaces and fuzzy frames are isomorphic from the categorical point of view. Meanwhile, we get that every fuzzy frame equipped with the strong L-filter convex structure is an injective balanced L- S 0 -convex space and conversely, the specialization L-ordered set of an injective balanced L- S 0 -convex space is a fuzzy frame.
- Published
- 2022
36. Formation Control of Multiagent Networks: Cooperative and Antagonistic Interactions
- Author
-
Tingwen Huang, Zhen Li, Shiping Wen, and Yang Tang
- Subjects
Convex analysis ,Computer science ,Process (computing) ,Regular polygon ,Polytope ,Matrix perturbation theory ,Topology ,Computer Science Applications ,Human-Computer Interaction ,Control and Systems Engineering ,Position (vector) ,08 Information and Computing Sciences, 09 Engineering ,Artificial Intelligence & Image Processing ,Electrical and Electronic Engineering ,Control (linguistics) ,Software - Abstract
This article studies the formation control problem of second-order multiagent networks, in which cooperative and antagonistic interactions of the agents spontaneously coexist in the communication process. Based on the convex analysis theory, several convex polytopes that do not require some kinds of system constraints are constructed in the presence of these interactions. Then, the matrix perturbation theory and some mathematical techniques are utilized to analyze these convex polytopes. The obtained results show that the agents with cooperative interactions monotonously converge to their own specified formation shape while maintaining the desired relative position of the other agents with antagonistic interactions. Subsequently, two numerical examples are presented to illustrate the obtained results.
- Published
- 2022
37. Dynamic Coverage Control Based on K-Means
- Author
-
C. L. Philip Chen, Wenjie Bai, Zhen Wang, Dengxiu Yu, and Hao Xu
- Subjects
Mathematical optimization ,Control and Systems Engineering ,Voronoi partition ,Computer science ,Control (management) ,Regular polygon ,k-means clustering ,Stability (learning theory) ,Coverage control ,Electrical and Electronic Engineering ,Space (mathematics) ,Sliding mode control - Abstract
In this paper, we propose the dynamic coverage control method based on K-means. In the traditional coverage control, Voronoi partition method is used to assign the coverage positions for intelligent units. However, the Voronoi partition method requires that the space to be covered is compact and convex, and it is difficult to realize the coverage control of high-dimensional space. Therefore, in this paper we propose a dynamic coverage planning algorithm based on K-means, which relaxes the requirements on the coverage objects and can calculate the optimal coverage positions of the intelligent units. In addition, we also design a dynamic coverage control law based on discrete sliding mode control to drive the intelligent units to the optimal coverage positions. Through the combination of planning algorithm and control law, the optimal coverage control performance can be achieved for the specified targets and the specified area, which can be discrete, non-convex and high-dimensional. The stability of the planning algorithm and the control law are proved by theoretical deduction. And the effectiveness and superiority of the dynamic coverage control method are verified by two examples.
- Published
- 2022
38. Global Convergence Guarantees of (A)GIST for a Family of Nonconvex Sparse Learning Problems
- Author
-
Hengmin Zhang, Jian Yang, Feng Qian, Wenli Du, Fanhua Shang, and Jianjun Qian
- Subjects
Mathematical optimization ,Databases, Factual ,Gastrointestinal Stromal Tumors ,Property (programming) ,Computer science ,Regular polygon ,Extrapolation ,Acceleration (differential geometry) ,Solver ,Synthetic data ,Computer Science Applications ,Human-Computer Interaction ,Control and Systems Engineering ,Convergence (routing) ,Humans ,Electrical and Electronic Engineering ,Algorithms ,Software ,Information Systems ,Shrinkage - Abstract
In recent years, most of the studies have shown that the generalized iterated shrinkage thresholdings (GISTs) have become the commonly used first-order optimization algorithms in sparse learning problems. The nonconvex relaxations of the l₀-norm usually achieve better performance than the convex case (e.g., l₁-norm) since the former can achieve a nearly unbiased solver. To increase the calculation efficiency, this work further provides an accelerated GIST version, that is, AGIST, through the extrapolation-based acceleration technique, which can contribute to reduce the number of iterations when solving a family of nonconvex sparse learning problems. Besides, we present the algorithmic analysis, including both local and global convergence guarantees, as well as other intermediate results for the GIST and AGIST, denoted as (A)GIST, by virtue of the Kurdyka-Łojasiewica (KŁ) property and some milder assumptions. Numerical experiments on both synthetic data and real-world databases can demonstrate that the convergence results of objective function accord to the theoretical properties and nonconvex sparse learning methods can achieve superior performance over some convex ones.
- Published
- 2022
39. Tractable Convex Approximations for Distributionally Robust Joint Chance-Constrained Optimal Power Flow Under Uncertainty
- Author
-
Wenchuan Wu, Lun Yang, Hongbin Sun, and Yinliang Xu
- Subjects
Mathematical optimization ,Computer science ,media_common.quotation_subject ,Regular polygon ,Energy Engineering and Power Technology ,Ambiguity ,Unimodality ,Set (abstract data type) ,Constraint (information theory) ,symbols.namesake ,Bonferroni correction ,symbols ,Probability distribution ,A priori and a posteriori ,Electrical and Electronic Engineering ,media_common - Abstract
Uncertainties arising from renewable energy bring huge challenges in optimal power flow (OPF) analysis. Various chance constrained approaches are proposed to manage the uncertainties in OPF models. However, most existing approaches assume that the probability distributions of uncertainties are known \emph{a priori}, or consider chance constraint individually. This paper proposes a distributionally robust (DR) joint chance constrained OPF model, which ensures that all the operation constraints are satisfied with a given probability and does not require the assumption on specific probability distributions. An ambiguity set built on the first and second moments is used to model the uncertainties. An optimized Bonferroni approximation (OBA) is first introduced to decompose the DR joint chance constraint into DR individual chance constraints, the resulting OBA formulation is strongly non-convex. Different convex approximations are then proposed to formulate the OBA based DR individual chance constraints as tractable formulations. The proposed convex approximations can be easily extended to incorporate the structural information associated with uncertainties like unimodality and symmetry. Case studies demonstrate the effectiveness of the proposed convex approximation methods.
- Published
- 2022
40. Unsupervised Feature Selection With Constrained ℓ₂,₀-Norm and Optimized Graph
- Author
-
Feiping Nie, Lai Tian, Xuelong Li, Rong Wang, and Xia Dong
- Subjects
Computational complexity theory ,Computer Networks and Communications ,Computer science ,Regular polygon ,Similarity matrix ,Feature selection ,02 engineering and technology ,Regularization (mathematics) ,Graph ,Computer Science Applications ,Sparse learning ,Artificial Intelligence ,Norm (mathematics) ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Algorithm ,Software - Abstract
In this article, we propose a novel feature selection approach, named unsupervised feature selection with constrained l2,0-norm (row-sparsity constrained) and optimized graph (RSOGFS), which unifies feature selection and similarity matrix construction into a general framework instead of independently performing the two-stage process; thus, the similarity matrix preserving the local manifold structure of data can be determined adaptively. Unlike those sparse learning-based feature selection methods that can only solve the relaxation or approximation problems by introducing sparsity regularization term into the objective function, the proposed method directly tackles the original l2,0-norm constrained problem to achieve group feature selection. Two optimization strategies are provided to solve the original sparse constrained problem. The convergence and approximation guarantees for the new algorithms are rigorously proved, and the computational complexity and parameter determination are theoretically analyzed. Experimental results on real-world data sets show that the proposed method for solving a nonconvex problem is superior to the state of the arts for solving the relaxed or approximate convex problems.
- Published
- 2022
41. New bounds for subset selection from conic relaxations
- Author
-
José Neto and Walid Ben-Ameur
- Subjects
Information Systems and Management ,General Computer Science ,Selection (relational algebra) ,Regular polygon ,Management Science and Operations Research ,Upper and lower bounds ,Industrial and Manufacturing Engineering ,Combinatorics ,Constraint (information theory) ,Cardinality ,Residual sum of squares ,Conic section ,Modeling and Simulation ,Mathematics - Abstract
New bounds are proposed for the subset selection problem which consists in minimizing the residual sum of squares subject to a cardinality constraint on the maximum number of non-zero variables. They rely on new convex relaxations providing both upper and lower bounds that are compared with others present in the literature. The performance of these methods is illustrated through computational experiments.
- Published
- 2022
42. Maximal directions of monotonicity of an aggregation function
- Author
-
H. De Meyer and B. De Baets
- Subjects
0209 industrial biotechnology ,Pure mathematics ,Logic ,Diagonal ,Regular polygon ,Monotonic function ,02 engineering and technology ,Function (mathematics) ,Unit square ,Main diagonal ,020901 industrial engineering & automation ,Artificial Intelligence ,0202 electrical engineering, electronic engineering, information engineering ,Piecewise ,020201 artificial intelligence & image processing ,Differentiable function ,Mathematics - Abstract
We introduce the concept of maximal directions of increasingness (resp. decreasingness) of an aggregation function. In the bivariate case, we derive these maximal directions with respect to points on the main diagonal of the unit square for a symmetric aggregation function that has either piecewise convex or piecewise concave level curves and is differentiable up to second order. With any bivariate aggregation function of the latter type we associate another bivariate aggregation function that has the same maximal directions of increasingness (resp. decreasingness) while having straight lines as level curves. We explore under which conditions the latter aggregation function is a semi-copula, a quasi-copula or a copula. As a by-product we establish a new construction method for aggregation functions with given diagonal section.
- Published
- 2022
43. Resilient Reliable Hθ Load Frequency Control of Power System With Random Gain Fluctuations
- Author
-
Subramanian Kuppusamy and Young Hoon Joo
- Subjects
Automatic frequency control ,Regular polygon ,Computer Science Applications ,Human-Computer Interaction ,Electric power system ,Exponential stability ,Control and Systems Engineering ,Bernoulli distribution ,Control theory ,Electrical and Electronic Engineering ,Actuator ,Random variable ,Software ,Reciprocal ,Mathematics - Abstract
This article proposes a resilient reliable H∞ load frequency control (LFC) design for power system involving the external load disturbances, stochastic actuator failures, and randomly occurring gain fluctuations. In this regard, the separate random variables are introduced which characterize the actuator failures and gain fluctuations in an individual manner that satisfies the Bernoulli distribution. The resilient reliable proportional-integral (PI)-type LFC is proposed by utilizing the resilient control scheme and the reciprocal convex technique along with the Lyapunov-Krasovskii functional (LKF), which guarantees the mean-square asymptotic stability of power system via H∞ performance index. Finally, the simulations are given to ensure the less conservative results of the proposed method when compared to the existing results.
- Published
- 2022
44. Catching all geodesics of a manifold with moving balls and application to controllability of the wave equation
- Author
-
Cyril Letrouit, Control And GEometry (CaGE ), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jacques-Louis Lions (LJLL (UMR_7598)), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Laboratoire Jacques-Louis Lions (LJLL (UMR_7598)), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Département de Mathématiques et Applications - ENS Paris (DMA), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU), Université Paris Diderot - Paris 7 (UPD7)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Diderot - Paris 7 (UPD7)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), École normale supérieure - Paris (ENS Paris)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Universités, Université Paris Diderot - Paris 7 (UPD7)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Sorbonne Université, and École normale supérieure - Paris (ENS Paris)
- Subjects
Geodesic ,[MATH.MATH-DS]Mathematics [math]/Dynamical Systems [math.DS] ,Regular polygon ,Dynamical Systems (math.DS) ,Riemannian manifold ,Absolute continuity ,Wave equation ,Domain (mathematical analysis) ,Manifold ,Theoretical Computer Science ,Combinatorics ,Mathematics - Analysis of PDEs ,Mathematics (miscellaneous) ,Optimization and Control (math.OC) ,FOS: Mathematics ,[MATH.MATH-AP]Mathematics [math]/Analysis of PDEs [math.AP] ,Ball (mathematics) ,Mathematics::Differential Geometry ,[MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC] ,Mathematics - Dynamical Systems ,Mathematics - Optimization and Control ,Analysis of PDEs (math.AP) ,Mathematics - Abstract
We address the problem of catching all speed $1$ geodesics of a Riemannian manifold with a moving ball: given a compact Riemannian manifold $(M,g)$ and small parameters $\varepsilon>0$ and $v>0$, is it possible to find $T>0$ and an absolutely continuous map $x:[0,T]\rightarrow M, t\mapsto x(t)$ satisfying $\|\dot{x}\|_{\infty}\leq v$ and such that any geodesic of $(M,g)$ traveled at speed $1$ meets the open ball $B_g(x(t),\varepsilon)\subset M$ within time $T$? Our main motivation comes from the control of the wave equation: our results show that the controllability of the wave equation can sometimes be improved by allowing the domain of control to move adequately, even very slowly. We first prove that, in any Riemannian manifold $(M,g)$ satisfying a geodesic recurrence condition (GRC), our problem has a positive answer for any $\varepsilon>0$ and $v>0$, and we give examples of Riemannian manifolds $(M,g)$ for which (GRC) is satisfied. Then, we build an explicit example of a domain $X\subset\mathbb{R}^2$ (with flat metric) containing convex obstacles, not satisfying (GRC), for which our problem has a negative answer if $��$ and $v$ are small enough, i.e., no sufficiently small ball moving sufficiently slowly can catch all geodesics of $X$., Annali della Scuola Normale Superiore di Pisa, Classe di Scienze, Scuola Normale Superiore In press
- Published
- 2023
45. Combination of Concave and Convex Paraboloids: Theoretical Model of New-type Daylighting System
- Author
-
Yusuke Tsuji and Hirotaka Suzuki
- Subjects
Mathematical optimization ,Regular polygon ,Type (model theory) ,Daylighting ,Mathematics - Published
- 2022
46. A new projection neural network for linear and convex quadratic second-order cone programming
- Author
-
Yaling Zhang and Hongwei Liu
- Subjects
Statistics and Probability ,Quadratic equation ,Artificial neural network ,Artificial Intelligence ,Computer science ,MathematicsofComputing_NUMERICALANALYSIS ,General Engineering ,Regular polygon ,Second-order cone programming ,Projection (set theory) ,Algorithm - Abstract
A new projection neural network approach is presented for the linear and convex quadratic second-order cone programming. In the method, the optimal conditions of the linear and convex second-order cone programming are equivalent to the cone projection equations. A Lyapunov function is given based on the G-norm distance function. Based on the cone projection function, the descent direction of Lyapunov function is used to design the new projection neural network. For the proposed neural network, we give the Lyapunov stability analysis and prove the global convergence. Finally, some numerical examples and two kinds of grasping force optimization problems are used to test the efficiency of the proposed neural network. The simulation results show that the proposed neural network is efficient for solving some linear and convex quadratic second-order cone programming problems. Especially, the proposed neural network can overcome the oscillating trajectory of the exist projection neural network for some linear second-order cone programming examples and the min-max grasping force optimization problem.
- Published
- 2022
47. Computing Essential Sets for Convex and Nonconvex Scenario Problems: Theory and Application
- Author
-
Xinbo Geng, M. Sadegh Modarresi, and Le Xie
- Subjects
0209 industrial biotechnology ,Mathematical optimization ,Control and Optimization ,Computer Networks and Communications ,Computer science ,020208 electrical & electronic engineering ,Regular polygon ,02 engineering and technology ,Dual (category theory) ,Set (abstract data type) ,Electric power system ,020901 industrial engineering & automation ,Power system simulation ,Cardinality ,Control and Systems Engineering ,Signal Processing ,0202 electrical engineering, electronic engineering, information engineering ,SIMPLE algorithm - Abstract
The scenario approach is a general data-driven algorithm to chance-constrained optimization. It seeks the optimal solution that is feasible to a carefully chosen number of scenarios. A crucial step in the scenario approach is to compute the cardinality of essential sets, which is the smallest subset of scenarios that determine the optimal solution. This paper addresses the challenge of efficiently identifying essential sets. For convex problems, we demonstrate that the sparsest dual solution of the scenario problem could pinpoint the essential set. For non-convex problems, we show that two simple algorithms return the essential set when the scenario problem is non-degenerate. Finally, we illustrate the theoretical results and computational algorithms on security-constrained unit commitment (SCUC) in power systems. In particular, case studies of chance-constrained SCUC are performed in the IEEE 118-bus system. Numerical results suggest that the scenario approach could be an attractive solution to practical power system applications.
- Published
- 2022
48. Convex Array With Variable Spacings (CAVS) for Ultrafast Ultrasound Diverging-Wave Imaging
- Author
-
Yang Jiao, Yujia Tang, Zhitian Shen, Zhangjian Li, Xinle Zhu, and Yaoyao Cui
- Subjects
Physics ,Variable (computer science) ,Optics ,business.industry ,Ultrasound ,Regular polygon ,Electrical and Electronic Engineering ,business ,Instrumentation ,Ultrashort pulse - Published
- 2022
49. Convex Synthesis of SNI Controllers Based on Frequency-Domain Data: MEMS Nanopositioner Example
- Author
-
Nastaran Nikooienejad and S. O. Reza Moheimani
- Subjects
Microelectromechanical systems ,SNi ,Control and Systems Engineering ,Computer science ,Control theory ,Frequency domain ,Regular polygon ,Electrical and Electronic Engineering - Published
- 2022
50. Automorphisms on normal and convex fuzzy truth values revisited
- Author
-
Susana Cubillo, Luis Magdalena, and Carmen Torres-Blanc
- Subjects
0209 industrial biotechnology ,Pure mathematics ,Logic ,Fuzzy set ,Normal function ,Regular polygon ,Boundary (topology) ,02 engineering and technology ,Function (mathematics) ,Automorphism ,Set (abstract data type) ,020901 industrial engineering & automation ,Artificial Intelligence ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Constant function ,Mathematics - Abstract
The present paper extends some previous works studying automorphisms in type-2 fuzzy sets. The framework for the analysis is the set of convex and normal functions from [ 0 , 1 ] to [ 0 , 1 ] (fuzzy truth values). The paper concentrates on those automorphisms that, in this framework, leave the constant function 1 fixed. This function is quite important since it defines the boundary between the functions that represent “TRUE” (increasing functions) and those that represent “FALSE” (decreasing functions), being at the same time the only normal function that is simultaneously increasing and decreasing. While C.L. Walker, E.A. Walker and J. Harding introduced in 2008 a family of functions leaving the constant function 1 fixed, the main goal of this paper is to prove that the functions of that family are in fact automorphisms, and moreover, that they are the only automorphisms (in the mentioned set of convex and normal functions from [ 0 , 1 ] to [ 0 , 1 ] ) that preserve the function 1.
- Published
- 2022
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