Sufficient conditions for convergence of the Sum
... In practice, there are two major obstacles in the application of LBP to concrete problems: (i) if LBP converges, it is not clear whether the results are a good approximation of the exact marginals; (ii) LBP does not always converge, and in these cases gives no approximations at all. These two issues ...
... In practice, there are two major obstacles in the application of LBP to concrete problems: (i) if LBP converges, it is not clear whether the results are a good approximation of the exact marginals; (ii) LBP does not always converge, and in these cases gives no approximations at all. These two issues ...
New Approach for the Cross-Dock Door Assignment Problem
... and xknq = ukn tkq . So if i = k and j = n and p = q , then xijp xknq = xijp , which becomes associated with the linear cost only. If i = k and either j ≠ n or p ≠ q , then xijp xknq = 0 . Thus, we run into trouble with solving the CDAP is goods are sent from origin i to destination i . Fortunately, ...
... and xknq = ukn tkq . So if i = k and j = n and p = q , then xijp xknq = xijp , which becomes associated with the linear cost only. If i = k and either j ≠ n or p ≠ q , then xijp xknq = 0 . Thus, we run into trouble with solving the CDAP is goods are sent from origin i to destination i . Fortunately, ...
Probabilistic Latent Semantic Analysis - KTI
... Let M be a utility matrix with people ratings for the movies The rows of M are people, the columns of M are movies The rows of U are people, the columns of U are concepts U connects people to concepts Then the rows of VT are concepts, the columns of VT are movies V connects movies to concepts Σ repr ...
... Let M be a utility matrix with people ratings for the movies The rows of M are people, the columns of M are movies The rows of U are people, the columns of U are concepts U connects people to concepts Then the rows of VT are concepts, the columns of VT are movies V connects movies to concepts Σ repr ...
Introduction to the non-asymptotic analysis of random matrices
... i Ai ⊗ Ai is the sample covariance matrix. If A has independent columns Aj , then A∗ A = (hAj , Ak i)j,k is the Gram matrix. Thus our analysis of the row-independent and column-independent models can be interpreted as a study of sample covariance matrices and Gram matrices of high dimensional distri ...
... i Ai ⊗ Ai is the sample covariance matrix. If A has independent columns Aj , then A∗ A = (hAj , Ak i)j,k is the Gram matrix. Thus our analysis of the row-independent and column-independent models can be interpreted as a study of sample covariance matrices and Gram matrices of high dimensional distri ...
Topology of Entanglement Evolution of Two Qubits
... The initial point is ~n (0) and, in the cases of interest here, the trajectory approaches a limiting point as t → ∞ and we can define ~n∞ = limt→∞ ~n(t). The entanglement evolution is the associated function C (t) = C (~n (t)). C ∈ [0, 1]. For studies of decoherence the main interest is in entanglem ...
... The initial point is ~n (0) and, in the cases of interest here, the trajectory approaches a limiting point as t → ∞ and we can define ~n∞ = limt→∞ ~n(t). The entanglement evolution is the associated function C (t) = C (~n (t)). C ∈ [0, 1]. For studies of decoherence the main interest is in entanglem ...
Multiple fundamental frequency estimation based on sparse
... suitable for the spectrum of music signals. In order to do so, we use the common features in the spectrum of a musical note. The magnitude of the Fourier transform of every note can be modeled by an impulse train at the fundamental frequency and at its harmonics, which has been multiplied with a sha ...
... suitable for the spectrum of music signals. In order to do so, we use the common features in the spectrum of a musical note. The magnitude of the Fourier transform of every note can be modeled by an impulse train at the fundamental frequency and at its harmonics, which has been multiplied with a sha ...
