Electronic portfolio is a method of teaching evaluation applied in teaching under the modern educational concept in recent years, normally used in primary and secondary schools. In recent years, some teachers have int...Electronic portfolio is a method of teaching evaluation applied in teaching under the modern educational concept in recent years, normally used in primary and secondary schools. In recent years, some teachers have introdnced it into university teaching. Characteristics and demands of fine arts education internship ask for application of the form of electronic portfolio internship for teaching evaluation, enabling instructors to make internship preparation for students＇ internship period. Teaching experience accumulation and reflection could bring a more systematic and comprehensive evaluation of teaching and internship can enhance internship students＇ awareness of the importance of internship, and make good sorting in internship effectively. Self-reflection and conclusion are conducive to exchange of internship experience, promotion of learning ability and cultivation of a good learning attitude of life-long learning. In internship evaluation, teachers can determine evaluation objectives by applying the electronic portfolio to help students select portfolio of internship, gather the information needed in teaching, make teaching recommendations, solve practical teaching problems, and finally make a multivariate evaluation of internship students, to really make art education internship contribute to students.展开更多
Regularized minimization problems with nonconvex, nonsmooth, even non-Lipschitz penalty functions have attracted much attention in recent years, owing to their wide applications in statistics, control,system identific...Regularized minimization problems with nonconvex, nonsmooth, even non-Lipschitz penalty functions have attracted much attention in recent years, owing to their wide applications in statistics, control,system identification and machine learning. In this paper, the non-Lipschitz ?_p(0 < p < 1) regularized matrix minimization problem is studied. A global necessary optimality condition for this non-Lipschitz optimization problem is firstly obtained, specifically, the global optimal solutions for the problem are fixed points of the so-called p-thresholding operator which is matrix-valued and set-valued. Then a fixed point iterative scheme for the non-Lipschitz model is proposed, and the convergence analysis is also addressed in detail. Moreover,some acceleration techniques are adopted to improve the performance of this algorithm. The effectiveness of the proposed p-thresholding fixed point continuation(p-FPC) algorithm is demonstrated by numerical experiments on randomly generated and real matrix completion problems.展开更多
文摘Electronic portfolio is a method of teaching evaluation applied in teaching under the modern educational concept in recent years, normally used in primary and secondary schools. In recent years, some teachers have introdnced it into university teaching. Characteristics and demands of fine arts education internship ask for application of the form of electronic portfolio internship for teaching evaluation, enabling instructors to make internship preparation for students＇ internship period. Teaching experience accumulation and reflection could bring a more systematic and comprehensive evaluation of teaching and internship can enhance internship students＇ awareness of the importance of internship, and make good sorting in internship effectively. Self-reflection and conclusion are conducive to exchange of internship experience, promotion of learning ability and cultivation of a good learning attitude of life-long learning. In internship evaluation, teachers can determine evaluation objectives by applying the electronic portfolio to help students select portfolio of internship, gather the information needed in teaching, make teaching recommendations, solve practical teaching problems, and finally make a multivariate evaluation of internship students, to really make art education internship contribute to students.
基金supported by National Natural Science Foundation of China(Grant Nos.11401124 and 71271021)the Scientific Research Projects for the Introduced Talents of Guizhou University(Grant No.201343)the Key Program of National Natural Science Foundation of China(Grant No.11431002)
文摘Regularized minimization problems with nonconvex, nonsmooth, even non-Lipschitz penalty functions have attracted much attention in recent years, owing to their wide applications in statistics, control,system identification and machine learning. In this paper, the non-Lipschitz ?_p(0 < p < 1) regularized matrix minimization problem is studied. A global necessary optimality condition for this non-Lipschitz optimization problem is firstly obtained, specifically, the global optimal solutions for the problem are fixed points of the so-called p-thresholding operator which is matrix-valued and set-valued. Then a fixed point iterative scheme for the non-Lipschitz model is proposed, and the convergence analysis is also addressed in detail. Moreover,some acceleration techniques are adopted to improve the performance of this algorithm. The effectiveness of the proposed p-thresholding fixed point continuation(p-FPC) algorithm is demonstrated by numerical experiments on randomly generated and real matrix completion problems.
