Given a large sparse matrix M, the method consists of fixing the rank to r and then looking for the closest rank-r matrix X to M, where the distance is measured in the Frobenius norm. A key element in the solution of this matrix nearness problem consists of the use of a constrained ...
One, introduced previously, uses a non-dimensional maximal vorticity norm. Two new indicators are then introduced using the Frobenius norm of the velocity gradient, and the eigenvalue modulus of the velocity gradient, both normalized by the maximum local grid spacing and free stream velocity. These...
This paper provides a computational procedure for a type of robust pole-placement problem. By exploiting the differentiability nature of the objective function based on the Frobenius norm condition number, the minimization problem is formulated in terms of a gradient flow to which standard ODE ...
We study the problem of training deep fully connected neural networks with Rectified Linear Unit (ReLU) activation function and cross entropy loss function
‖x‖ represents the Euclidean norm of a vector x, while ‖X‖ represents the Frobenius norm of a matrix X. Access through your organization Check access to the full text by signing in through your organization. Access through your organization ...
读书报告 | 谱图理论 Ch4: Adjacency matrices, Eigenvalue Interlacing, and the Perron-Frobenius Theorem 读书报告 | 谱图理论 Ch5: Comparing Graphs 读书报告 | 谱图理论 Ch6: Fundamental Graphs 读书报告 | 谱图理论 Ch7: Cayley Graphs 读书报告 | 谱图理论 Ch8: Eigenvalues of Random Graphs 读书报告...
For a complex matrix A, we use AT, AH, A¯,‖A‖2,‖A‖, λmax [A], λmin [A] to denote the transpose, the conjugate transpose, the conjugate, the spectral norm, the Frobenius norm, the maximal eigenvalue, the minimal nonzero eigenvalue, respectively. In is the identity ma...
In this paper,the concept of gradient matrix( F(X)) is presented,and an algorithm is constructed to solve the symmetric solution of the minimum Frobenius norm residual problem:min‖(A1XB1,A2XB2)-(C1,C2)‖. 该文提出了梯度矩阵(F(X))的概念,构造了一种迭代法求最小二乘问题min‖(A1XB1...
Matrix tri-factorization subject to binary constraints is a versatile and powerful framework for the simultaneous clustering of observations and features,
The target gradient field whose structure tensor approximates the aforementioned tensor in the Frobenius norm sense is then obtained. An enhanced image is finally reconstructed from the target gradient field by least square fitting. Applications can include fusion of results by different enhancement ...