To solve the global optimization problems which have several local minimizers,a new F-C function is proposes by combining a lled function and a cross function.The properties of the F-C function are discussed and the c...To solve the global optimization problems which have several local minimizers,a new F-C function is proposes by combining a lled function and a cross function.The properties of the F-C function are discussed and the corresponding algorithm is given in this paper.F-C function has the same local minimizers with the objective function.Therefore,the F-C function method only needs to minimize the objective function once in the rst iteration.Numerical experiments are performed and the results show that the proposed method is very effective.展开更多
Recovering an unknown high dimensional low rank matrix from a small set of entries is widely spread in the fields of machine learning,system identification and image restoration,etc.In many practical applications,the ...Recovering an unknown high dimensional low rank matrix from a small set of entries is widely spread in the fields of machine learning,system identification and image restoration,etc.In many practical applications,the few observations are always corrupted by noise and the noise level is also unknown.A novel model with nuclear norm and square root type estimator has been proposed,which does not rely on the knowledge or on an estimation of the standard deviation of the noise.In this paper,we firstly reformulate the problem to an equivalent variable separated form by introducing an auxiliary variable.Then we propose an efficient alternating direction method of multipliers(ADMM)for solving it.Both of resulting subproblems admit an explicit solution,which makes our algorithm have a cheap computing.Finally,the numerical results show the benefits of the model and the efficiency of the proposed method.展开更多
In paper [1],it was shown that an explicit expression of the cardinal basis functions for two-point Hermite interpolation. This paper will show the explicit expression of Hermite interpolation under the Ball basis.
We establish polynomial complexity corrector algorithms for linear programming over bounds of the Mehrotra-type predictor- symmetric cones. We first slightly modify the maximum step size in the predictor step of the s...We establish polynomial complexity corrector algorithms for linear programming over bounds of the Mehrotra-type predictor- symmetric cones. We first slightly modify the maximum step size in the predictor step of the safeguard based Mehrotra-type algorithm for linear programming, that was proposed by Salahi et al. Then, using the machinery of Euclidean Jordan algebras, we extend the modified algorithm to symmetric cones. Based on the Nesterov-Todd direction, we obtain O（r log ε1） iteration complexity bound of this algorithm, where r is the rank of the Jordan algebras and ε is the required precision. We also present a new variant of Mehrotra-type algorithm using a new adaptive updating scheme of centering parameter and show that this algorithm enjoys the same order of complexity bound as the safeguard algorithm. We illustrate the numerical behaviour of the methods on some small examples.展开更多
基金the National Natural Science Foundation of China(12071112,11471102)Basic Research Projects for Key Scientific Research Projects in Henan Province of China(20ZX001)。
基金the National Natural Science Foundation of China(12071112)and(11471102)the Basic Research Projects for Key Scientific Research Projects in Henan Province(20ZX001)the Research and Practice Project on Education and Teaching Reform in Henan Institute of Science and Technology(2021YB45)。
基金The National Natural Science Foundation of China (10571137 and 10571116)the Great Natural Science Foundation of Henan University of Science and Technology (2005ZD006)
基金Supported by National Natural Science Foundation of China(No.11471102)Basic research projects for key scientific research projects in Henan Province(No.20ZX001)。
文摘To solve the global optimization problems which have several local minimizers,a new F-C function is proposes by combining a lled function and a cross function.The properties of the F-C function are discussed and the corresponding algorithm is given in this paper.F-C function has the same local minimizers with the objective function.Therefore,the F-C function method only needs to minimize the objective function once in the rst iteration.Numerical experiments are performed and the results show that the proposed method is very effective.
基金Supported by the National Natural Science Foundation of China(Grant No.11971149,12101195,12071112,11871383)Natural Science Foundation of Henan Province for Youth(Grant No.202300410146).
文摘Recovering an unknown high dimensional low rank matrix from a small set of entries is widely spread in the fields of machine learning,system identification and image restoration,etc.In many practical applications,the few observations are always corrupted by noise and the noise level is also unknown.A novel model with nuclear norm and square root type estimator has been proposed,which does not rely on the knowledge or on an estimation of the standard deviation of the noise.In this paper,we firstly reformulate the problem to an equivalent variable separated form by introducing an auxiliary variable.Then we propose an efficient alternating direction method of multipliers(ADMM)for solving it.Both of resulting subproblems admit an explicit solution,which makes our algorithm have a cheap computing.Finally,the numerical results show the benefits of the model and the efficiency of the proposed method.
文摘In paper [1],it was shown that an explicit expression of the cardinal basis functions for two-point Hermite interpolation. This paper will show the explicit expression of Hermite interpolation under the Ball basis.
基金Supported by the National Natural Science Foundation of China(11471102,61301229)Supported by the Natural Science Foundation of Henan University of Science and Technology(2014QN039)
文摘We establish polynomial complexity corrector algorithms for linear programming over bounds of the Mehrotra-type predictor- symmetric cones. We first slightly modify the maximum step size in the predictor step of the safeguard based Mehrotra-type algorithm for linear programming, that was proposed by Salahi et al. Then, using the machinery of Euclidean Jordan algebras, we extend the modified algorithm to symmetric cones. Based on the Nesterov-Todd direction, we obtain O（r log ε1） iteration complexity bound of this algorithm, where r is the rank of the Jordan algebras and ε is the required precision. We also present a new variant of Mehrotra-type algorithm using a new adaptive updating scheme of centering parameter and show that this algorithm enjoys the same order of complexity bound as the safeguard algorithm. We illustrate the numerical behaviour of the methods on some small examples.
