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Numerical simulation of the fluid and flexible rods interaction using a semi-resolved coupling model promoted by anisotropic Gaussian kernel function 认领 引用
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作者 Caiping Jin Jingxin Zhang Yonglin Sun 《Theoretical & Applied Mechanics Letters》 EI CAS CSCD 2025年第1期5-8,共4页
The numerical simulation of the fluid flow and the flexible rod(s)interaction is more complicated and has lower efficiency due to the high computational cost.In this paper,a semi-resolved model coupling the computatio... The numerical simulation of the fluid flow and the flexible rod(s)interaction is more complicated and has lower efficiency due to the high computational cost.In this paper,a semi-resolved model coupling the computational fluid dynamics and the flexible rod dynamics is proposed using a two-way domain expansion method.The gov-erning equations of the flexible rod dynamics are discretized and solved by the finite element method,and the fluid flow is simulated by the finite volume method.The interaction between fluids and solid rods is modeled by introducing body force terms into the momentum equations.Referred to the traditional semi-resolved numerical model,an anisotropic Gaussian kernel function method is proposed to specify the interactive forces between flu-ids and solid bodies for non-circle rod cross-sections.A benchmark of the flow passing around a single flexible plate with a rectangular cross-section is used to validate the algorithm.Focused on the engineering applications,a test case of a finite patch of cylinders is implemented to validate the accuracy and efficiency of the coupled model. 展开更多
关键词 Semi-resolved coupling model Two-way domain expansion method Anisotropic Gaussian kernel function Flexible rod(s)
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SVM with Quadratic Polynomial Kernel Function Based Nonlinear Model One-step-ahead Predictive Control 认领 引用 被引量：17
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作者 钟伟民 何国龙 +1 位作者 皮道映 孙优贤 《Chinese Journal of Chemical Engineering》 SCIE EI CAS 2005年第3期373-379,共7页
A support vector machine (SVM) with quadratic polynomial kernel function based nonlinear model one-step-ahead predictive controller is presented. The SVM based predictive model is established with black-box identifica... A support vector machine (SVM) with quadratic polynomial kernel function based nonlinear model one-step-ahead predictive controller is presented. The SVM based predictive model is established with black-box identification method. By solving a cubic equation in the feature space, an explicit predictive control law is obtained through the predictive control mechanism. The effect of controller is demonstrated on a recognized benchmark problem and on the control of continuous-stirred tank reactor (CSTR). Simulation results show that SVM with quadratic polynomial kernel function based predictive controller can be well applied to nonlinear systems, with good performance in following reference trajectory as well as in disturbance-rejection. 展开更多
关键词 nonlinear model predictive control support vector machine nonlinear system identification kernel function
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Landslide prediction based on improved principal component analysis and mixed kernel function least squares support vector regression model 认领 引用 被引量：8
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作者 LI Li-min CHENG Shao-kang WEN Zong-zhou 《Journal of Mountain Science》 SCIE CSCD 2021年第8期2130-2142,共13页
Landslide probability prediction plays an important role in understanding landslide information in advance and taking preventive measures.Many factors can influence the occurrence of landslides,which is easy to have a... Landslide probability prediction plays an important role in understanding landslide information in advance and taking preventive measures.Many factors can influence the occurrence of landslides,which is easy to have a curse of dimensionality and thus lead to reduce prediction accuracy.Then the generalization ability of the model will also decline sharply when there are only small samples.To reduce the dimension of calculation and balance the model’s generalization and learning ability,this study proposed a landslide prediction method based on improved principal component analysis(PCA)and mixed kernel function least squares support vector regression(LSSVR)model.First,the traditional PCA was introduced with the idea of linear discrimination,and the dimensions of initial influencing factors were reduced from 8 to 3.The improved PCA can not only weight variables but also extract the original feature.Furthermore,combined with global and local kernel function,the mixed kernel function LSSVR model was framed to improve the generalization ability.Whale optimization algorithm(WOA)was used to optimize the parameters.Moreover,Root Mean Square Error(RMSE),the sum of squared errors(SSE),Mean Absolute Error(MAE),Mean Absolute Precentage Error(MAPE),and reliability were employed to verify the performance of the model.Compared with radial basis function(RBF)LSSVR model,Elman neural network model,and fuzzy decision model,the proposed method has a smaller deviation.Finally,the landslide warning level obtained from the landslide probability can also provide references for relevant decision-making departments in emergency response. 展开更多
关键词 Landslide probability LSSVR Mixed kernel function Improved PCA Warning level
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An algorithm for moving target detection in IR image based on grayscale distribution and kernel function 认领 引用 被引量：7
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作者 王鲁平 张路平 +1 位作者 赵明 李飚 《Journal of Central South University》 SCIE EI CAS 2014年第11期4270-4278,共9页
A fast algorithm based on the grayscale distribution of infrared target and the weighted kernel function was proposed for the moving target detection(MTD) in dynamic scene of image series. This algorithm is used to de... A fast algorithm based on the grayscale distribution of infrared target and the weighted kernel function was proposed for the moving target detection(MTD) in dynamic scene of image series. This algorithm is used to deal with issues like the large computational complexity, the fluctuation of grayscale, and the noise in infrared images. Four characteristic points were selected by analyzing the grayscale distribution in infrared image, of which the series was quickly matched with an affine transformation model. The image was then divided into 32×32 squares and the gray-weighted kernel(GWK) for each square was calculated. At last, the MTD was carried out according to the variation of the four GWKs. The results indicate that the MTD can be achieved in real time using the algorithm with the fluctuations of grayscale and noise can be effectively suppressed. The detection probability is greater than 90% with the false alarm rate lower than 5% when the calculation time is less than 40 ms. 展开更多
关键词 moving target detection gray-weighted kernel function dynamic background
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A Full-Newton Step Feasible IPM for Semidefinite Optimization Based on a Kernel Function with Linear Growth Term 认领 引用 被引量：2
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作者 GENG Jie ZHANG Mingwang PANG Jinjuan 《Wuhan University Journal of Natural Sciences》 CAS CSCD 2020年第6期501-509,共9页
In this paper,we propose and analyze a full-Newton step feasible interior-point algorithm for semidefinite optimization based on a kernel function with linear growth term.The kernel function is used both for determini... In this paper,we propose and analyze a full-Newton step feasible interior-point algorithm for semidefinite optimization based on a kernel function with linear growth term.The kernel function is used both for determining the search directions and for measuring the distance between the given iterate and theμ-center for the algorithm.By developing a new norm-based proximity measure and some technical results,we derive the iteration bound that coincides with the currently best known iteration bound for the algorithm with small-update method.In our knowledge,this result is the first instance of full-Newton step feasible interior-point method for SDO which involving the kernel function. 展开更多
关键词 semidefinite optimization interior-point algorithm kernel function iteration complexity
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A Full-Newton Step Feasible Interior-Point Algorithm for the Special Weighted Linear Complementarity Problems Based on a Kernel Function 认领 引用 被引量：2
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作者 GENG Jie ZHANG Mingwang ZHU Dechun 《Wuhan University Journal of Natural Sciences》 CAS CSCD 2024年第1期29-37,共9页
In this paper,a new full-Newton step primal-dual interior-point algorithm for solving the special weighted linear complementarity problem is designed and analyzed.The algorithm employs a kernel function with a linear ... In this paper,a new full-Newton step primal-dual interior-point algorithm for solving the special weighted linear complementarity problem is designed and analyzed.The algorithm employs a kernel function with a linear growth term to derive the search direction,and by introducing new technical results and selecting suitable parameters,we prove that the iteration bound of the algorithm is as good as best-known polynomial complexity of interior-point methods.Furthermore,numerical results illustrate the efficiency of the proposed method. 展开更多
关键词 interior-point algorithm weighted linear complementarity problem full-Newton step kernel function iteration complexity
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Interior-point algorithm based on general kernel function for monotone linear complementarity problem 认领 引用
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作者 刘勇 白延琴 《Journal of Shanghai University(English Edition)》 2009年第2期95-101,共7页
A polynomial interior-point algorithm is presented for monotone linear complementarity problem （MLCP） based on a class of kernel functions with the general barrier term, which are called general kernel functions. Un... A polynomial interior-point algorithm is presented for monotone linear complementarity problem （MLCP） based on a class of kernel functions with the general barrier term, which are called general kernel functions. Under the mild conditions for the barrier term, the complexity bound of algorithm in terms of such kernel function and its derivatives is obtained. The approach is actually an extension of the existing work which only used the specific kernel functions for the MLCP. 展开更多
关键词 monotone linear complementarity problem （MLCP） interior-point method kernel function polynomial complexity
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Interior-Point Algorithm for Linear Optimization Based on a New Kernel Function 认领 引用 被引量：2
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作者 CHEN Donghai ZHANG Mingwang LI Weihua 《Wuhan University Journal of Natural Sciences》 CAS 2012年第1期12-18,共7页
In this paper, we design a primal-dual interior-point algorithm for linear optimization. Search directions and proximity function are proposed based on a new kernel function which includes neither growth term nor barr... In this paper, we design a primal-dual interior-point algorithm for linear optimization. Search directions and proximity function are proposed based on a new kernel function which includes neither growth term nor barrier term. Iteration bounds both for large-and small-update methods are derived, namely, O（nlog（n/c）） and O（√nlog（n/ε））. This new kernel function has simple algebraic expression and the proximity function has not been used before. Analogous to the classical logarithmic kernel function, our complexity analysis is easier than the other pri- mal-dual interior-point methods based on logarithmic barrier functions and recent kernel functions. 展开更多
关键词 linear optimization interior-point algorithms pri- mal-dual methods kernel function polynomial complexity
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Multi-output Gaussian Process Regression Model with Combined Kernel Function for Polyester Esterification Processes 认领 引用
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作者 王恒骞 耿君先 陈磊 《Journal of Donghua University(English Edition)》 CAS 2023年第1期27-33,共7页
In polyester fiber industrial processes,the prediction of key performance indicators is vital for product quality.The esterification process is an indispensable step in the polyester polymerization process.It has the ... In polyester fiber industrial processes,the prediction of key performance indicators is vital for product quality.The esterification process is an indispensable step in the polyester polymerization process.It has the characteristics of strong coupling,nonlinearity and complex mechanism.To solve these problems,we put forward a multi-output Gaussian process regression(MGPR)model based on the combined kernel function for the polyester esterification process.Since the seasonal and trend decomposition using loess(STL)can extract the periodic and trend characteristics of time series,a combined kernel function based on the STL and the kernel function analysis is constructed for the MGPR.The effectiveness of the proposed model is verified by the actual polyester esterification process data collected from fiber production. 展开更多
关键词 seasonal and trend decomposition using loess(STL) multi-output Gaussian process regression combined kernel function polyester esterification process
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Kernel Function-Based Primal-Dual Interior-Point Methods for Symmetric Cones Optimization 认领 引用
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作者 ZHAO Dequan ZHANG Mingwang 《Wuhan University Journal of Natural Sciences》 CAS 2014年第6期461-468,共8页
In this paper, we present a large-update primal-dual interior-point method for symmetric cone optimization（SCO） based on a new kernel function, which determines both search directions and the proximity measure betwe... In this paper, we present a large-update primal-dual interior-point method for symmetric cone optimization（SCO） based on a new kernel function, which determines both search directions and the proximity measure between the iterate and the center path. The kernel function is neither a self-regular function nor the usual logarithmic kernel function. Besides, by using Euclidean Jordan algebraic techniques, we achieve the favorable iteration complexity O（ √r（1/2）（log r）^2 log（r/ ε））, which is as good as the convex quadratic semi-definite optimization analogue. 展开更多
关键词 symmetric cones optimization Kernel function Interior-point method polynomial complexity
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AN ASYMPTOTIC EXPANSION FORMULA OF KERNEL FUNCTION FOR QUASI FOURIER-LEGENDRE SERIES AND ITS APPLICATION 认领 引用
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作者 Zhang Peixuan 《Analysis in Theory and Applications》 1997年第1期33-42,共10页
Is this paper we shall give cm asymptotic expansion formula of the kernel functim for the Quasi Faurier-Legendre series on an ellipse, whose error is 0(12) and then applying it we shall sham an analogue of an exact re... Is this paper we shall give cm asymptotic expansion formula of the kernel functim for the Quasi Faurier-Legendre series on an ellipse, whose error is 0(12) and then applying it we shall sham an analogue of an exact result in trigonometric series. 展开更多
关键词 AN ASYMPTOTIC EXPANSION FORMULA OF KERNEL FUNCTION FOR QUASI FOURIER-LEGENDRE SERIES AND ITS APPLICATION Math ITS
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Using mixed kernel support vector machine to improve the predictive accuracy of genome selection 认领 引用 被引量：2
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作者 Jinbu Wang Wencheng Zong +6 位作者 Liangyu Shi Mianyan Li Jia Li Deming Ren Fuping Zhao Lixian Wang Ligang Wang 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2026年第2期775-787,共13页
The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects acc... The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects accurately.Machine learning models have demonstrated remarkable potential in addressing these challenges.In this study,we introduced the concept of mixed kernel functions to explore the performance of support vector machine regression(SVR) in GS.Six single kernel functions(SVR_L,SVR_C,SVR_G,SVR_P,SVR_S,SVR_L) and four mixed kernel functions(SVR_GS,SVR_GP,SVR_LS,SVR_LP) were used to predict genome breeding values.The prediction accuracy,mean squared error(MSE) and mean absolute error(MAE) were used as evaluation indicators to compare with two traditional parametric models(GBLUP,BayesB) and two popular machine learning models(RF,KcRR).The results indicate that in most cases,the performance of the mixed kernel function model significantly outperforms that of GBLUP,BayesB and single kernel function.For instance,for T1 in the pig dataset,the predictive accuracy of SVR_GS is improved by 10% compared to GBLUP,and by approximately 4.4 and 18.6% compared to SVR_G and SVR_S respectively.For E1 in the wheat dataset,SVR_GS achieves 13.3% higher prediction accuracy than GBLUP.Among single kernel functions,the Laplacian and Gaussian kernel functions yield similar results,with the Gaussian kernel function performing better.The mixed kernel function notably reduces the MSE and MAE when compared to all single kernel functions.Furthermore,regarding runtime,SVR_GS and SVR_GP mixed kernel functions run approximately three times faster than GBLUP in the pig dataset,with only a slight increase in runtime compared to the single kernel function model.In summary,the mixed kernel function model of SVR demonstrates speed and accuracy competitiveness,and the model such as SVR_GS has important application potential for GS. 展开更多
关键词 genome selection machine learning support vector machine kernel function mixed kernel function
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An Efficient Hyperbolic Kernel Function Yielding the Best Known Iteration Bounds for Linear Programming 认领 引用
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作者 Imene TOUIL Wided CHIKOUCHE +1 位作者 Djamel BENTERKI Amina ZERARI 《Acta Mathematicae Applicatae Sinica》 SCIE CSCD 2025年第1期133-151,共19页
Interior-point methods(IPMs) for linear programming(LP) are generally based on the logarithmic barrier function. Peng et al.(J. Comput. Technol. 6: 61–80, 2001) were the first to propose non-logarithmic kernel functi... Interior-point methods(IPMs) for linear programming(LP) are generally based on the logarithmic barrier function. Peng et al.(J. Comput. Technol. 6: 61–80, 2001) were the first to propose non-logarithmic kernel functions(KFs) for solving IPMs. These KFs are strongly convex and smoothly coercive on their domains.Later, Bai et al.(SIAM J. Optim. 15(1): 101–128, 2004) introduced the first KF with a trigonometric barrier term. Since then, no new type of KFs were proposed until 2020, when Touil and Chikouche(Filomat. 34(12):3957–3969, 2020;Acta Math. Sin.(Engl. Ser.), 38(1): 44–67, 2022) introduced the first hyperbolic KFs for semidefinite program(ming(SD)P). They( establishe)d that the iteration complexities of algorithms based on their proposed KFs are O(n2/3log(n/ε) and O(n3/4log(n/ε)) for large-update methods, respectively. The aim of this work is to improve the complexity result for large-update method. In fact, we present a new parametric KF with a hyperbolic barrier term. By simple tools, we show that the worst-case iteration complexity of our algorithm for the large-update method is O(√n log n log(n/ε)) iterations. This coincides with the currently best-known iteration bounds for IPMs based on all existing kind of KFs.The algorithm based on the proposed KF has been tested. Extensive numerical simulations on test problems with different sizes have shown that this KF has promising results. 展开更多
关键词 linear programming primal-dual interior-point methods kernel functions complexity analysis large and small-update methods
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An approach of dynamic response analysis of nonlinear structures based on least square Volterra kernel function identification 认领 引用 被引量：1
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作者 Zhenhao Zhang Jun Xiong +4 位作者 Zhenpeng Zhao Fuming Wang Yi Zeng Bing Zhao Lu Ke 《Transportation Safety and Environment》 EI 2023年第2期1-13,共13页
Analysis of the dynamic response of a complex nonlinear system is always a difficult problem.By using Volterra functional series to describe a nonlinear system,its response analysis can be similar to using Fourier/Lap... Analysis of the dynamic response of a complex nonlinear system is always a difficult problem.By using Volterra functional series to describe a nonlinear system,its response analysis can be similar to using Fourier/Laplace transform and linear transfer function method to analyse a linear system’s response.In this paper,a dynamic response analysis method for nonlinear systems based on Volterra series is developed.Firstly,the recursive formula of the least square method is established to solve the Volterra kernel function vector,and the corresponding MATLAB programme is compiled.Then,the Volterra kernel vector corresponding to the nonlinear response of a structure under seismic excitation is identified,and the accuracy and applicability of using the kernel vector to predict the response of a nonlinear structure are analysed.The results show that the Volterra kernel function identified by the derived recursive formula can accurately describe the nonlinear response characteristics of a structure under an excitation.For a general nonlinear system,the first three order Volterra kernel function can relatively accurately express its nonlinear response characteristics.In addition,the obtained Volterra kernel function can be used to accurately predict the nonlinear response of a structure under the similar type of dynamic load. 展开更多
关键词 nonlinear system Volterra series kernel function dynamic response least square method
A Large-update Interior-point Algorithm for Convex Quadratic Semi-definite Optimization Based on a New Kernel Function 认领 引用 被引量：9
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作者 Ming Wang ZHANG 《Acta Mathematica Sinica,English Series》 SCIE CSCD 2012年第11期2313-2328,共16页
In this paper, we present a large-update interior-point algorithm for convex quadratic semi-definite optimization based on a new kernel function. The proposed function is strongly convex. It is not self-regular functi... In this paper, we present a large-update interior-point algorithm for convex quadratic semi-definite optimization based on a new kernel function. The proposed function is strongly convex. It is not self-regular function and also the usual logarithmic function. The goal of this paper is to investigate such a kernel function and show that the algorithm has favorable complexity bound in terms of the elegant analytic properties of the kernel function. The complexity bound is shown to be O（√n（logn）2 log e）. This bound is better than that by the classical primal-dual interior-point methods based on logarithmic barrier function and in optimization fields. Some computational results recent kernel functions introduced by some authors have been provided. 展开更多
关键词 Convex quadratic semi-definite optimization kernel function interior-point algorithm^large-update method complexity
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Complexity Analysis of an Interior Point Algorithm for the Semidefinite Optimization Based on a Kernel Function with a Double Barrier Term 认领 引用 被引量：2
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作者 Mohamed ACHACHE 《Acta Mathematica Sinica,English Series》 SCIE CSCD 2015年第3期543-556,共14页
In this paper, we establish the polynomial complexity of a primal-dual path-following interior point algorithm for solving semidefinite optimization（SDO） problems. The proposed algorithm is based on a new kernel fun... In this paper, we establish the polynomial complexity of a primal-dual path-following interior point algorithm for solving semidefinite optimization（SDO） problems. The proposed algorithm is based on a new kernel function which differs from the existing kernel functions in which it has a double barrier term. With this function we define a new search direction and also a new proximity function for analyzing its complexity. We show that if q1 〉 q2 〉 1, the algorithm has O（（q1 ＋ 1） nq1＋1/2（q1-q2）logn/ε）and O（（q1 ＋ 1）2（q1-q2）^3q1-2q2＋1√n logn/c） complexity results for large- and small-update methods, respectively. 展开更多
关键词 Semidefinite optimization kernel functions primal-dual interior point methods large andsmall-update algorithms complexity of algorithms
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A Path-Following Full Newton-Step Infeasible Interior-Point Algorithm for P∗(κ)-HLCPs Based on a Kernel Function 认领 引用
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作者 Soodabeh Asadi Hossein Mansouri Maryam Zangiabadi 《Journal of the Operations Research Society of China》 EI CSCD 2016年第1期77-96,共20页
In this paper,we present a path-following infeasible interior-point method for P∗(κ)horizontal linear complementarity problems(P∗(κ)-HLCPs).The algorithm is based on a simple kernel function for finding the search d... In this paper,we present a path-following infeasible interior-point method for P∗(κ)horizontal linear complementarity problems(P∗(κ)-HLCPs).The algorithm is based on a simple kernel function for finding the search directions and defining the neighborhood of the central path.The algorithm follows the central path related to some perturbations of the original problem,using the so-called feasibility and centering steps,along with only full such steps.Therefore,it has the advantage that the calculation of the step sizes at each iteration is avoided.The complexity result shows that the full-Newton step infeasible interior-point algorithm based on the simple kernel function enjoys the best-known iteration complexity for P∗(κ)-HLCPs. 展开更多
关键词 Horizontal linear complementarity problem Infeasible interior-point method Central path Kernel function
Bayesian optimized support vector regression with a Gaussian kernel for accurate prediction of the state of health of lithium-ion batteries used for electric vehicle applications 认领 引用
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作者 Selvaraj Vedhanayaki Vairavasundaram Indragandhi 《Global Energy Interconnection》 EI CSCD 2025年第5期891-904,共14页
The state of health SoH of lithium ion batteries plays a predominant role in ensuring the safe and reliable operation of electric vehicles.In this,a novel SoH estimation approach using support vector regression with a... The state of health SoH of lithium ion batteries plays a predominant role in ensuring the safe and reliable operation of electric vehicles.In this,a novel SoH estimation approach using support vector regression with a Gaussian kernel optimized using the Bayesian optimization technique(BO-SVR with a Gaussian kernel)was proposed.Unlike,traditional approaches that use the internal resistance,and battery capacity as input parameters,this study utilized the equivalent discharging voltage difference interval and equivalent charging voltage difference interval,as they capture the dynamic voltage characteristics associated with the battery degradation.The model was simulated using MATLAB 2023a.The mean absolute error,R2,root mean squared error,and mean squared error were considered as performance indicators.The simulation results indicated that the proposed BO-SVR with a Gaussian kernel model had superior performance to other kernel SVR and Gaussian Process Regression models,with a reduced RMSE of 0.0082,thus demonstrating its potential to predict the SoH more accurately. 展开更多
关键词 Lithium-ion batteries State of health Machine learning algorithms Bayesian optimization Kernel function
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Zero Problems of the Bergman Kernel Function on the First Type of Cartan-Hartogs Domain 认领 引用 被引量：3
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作者 Xin ZHAO An WANG 《Chinese Annals of Mathematics,Series B》 SCIE CSCD 2022年第2期265-280,共16页
The authors give the condition that the Bergman kernel function on the first type of Cartan-Hartogs domain exists zeros.If the Bergman kernel function of this type of domain has zeros,the zero set is composed of sever... The authors give the condition that the Bergman kernel function on the first type of Cartan-Hartogs domain exists zeros.If the Bergman kernel function of this type of domain has zeros,the zero set is composed of several path-connected branches,and there exists a continuous curve to connect any two points in the non-zero set. 展开更多
关键词 Cartan-Hartogs domain Zeros of Bergman kernel function Path connectivity
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An Efficient Parameterized Logarithmic Kernel Function for Semidefinite Optimization 认领 引用 被引量：1
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作者 Louiza DERBAL Zakia KEBBICHE 《Acta Mathematicae Applicatae Sinica》 SCIE CSCD 2020年第3期753-770,共18页
In this paper,we present a primal-dual interior point algorithm for semidefinite optimization problems based on a new class of kernel functions.These functions constitute a combination of the classic kernel function a... In this paper,we present a primal-dual interior point algorithm for semidefinite optimization problems based on a new class of kernel functions.These functions constitute a combination of the classic kernel function and a barrier term.We derive the complexity bounds for large and small-update methods respectively.We show that the best result of iteration bounds for large and small-update methods can be achieved,namely O(q√n(log√n)^q+1/q logn/ε)for large-update methods and O(q^3/2(log√q)^q+1/q√nlogn/ε)for small-update methods.We test the efficiency and the validity of our algorithm by running some computational tests,then we compare our numerical results with results obtained by algorithms based on different kernel functions. 展开更多
关键词 kernel function interior-point algorithms semidefinite optimization complexity bound primaldual methods
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