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Iterative deconvolution algorithm with quadratic convergence
Journal of the Optical Society of America A, 1987Deconvolution is an important problem in many branches of physics and engineering, and many different algorithms have been considered for deconvolving two signals. Iterative algorithms based on the method of successive approximations have become popular for signal deconvolution because of the flexibility that they allow for the incorporation of signal ...
C. E. Morris +2 more
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Quadratically Convergent Methods
2002The succesive approximation method as well as the monotone iterative methods described previously are not very fast. To explain this let us consider an operator T : X → X. All these methods give us convergent sequences (u k ) of the form $${u_{k + 1}} = T({u_k}),k \in N$$ having as limit a fixed point u * of T.
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Convergence property of quadratic neural network
Annual Meeting Optical Society of America, 1989Use of the linear discriminant function in the first-order neural network (e.g., the Hopfield model) has drawbacks in that the first-order correlation signal is easily interfered by the low-correlated random noise and this kind of neural network has limited storage capacity. High-order neural networks that can effectively suppress the noise with a high-
Shaoping Bian, Kebin Xu, Jing Hong
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Global convergence of phase retrieval by quadratic approach
IEEE Transactions on Antennas and Propagation, 2005The reliability of two different implementations of a quadratic phase retrieval approach to the problem of determining the far field of a radiating system from only square amplitude information on the near field zone is studied. The first implementation exploits square amplitude data over two scanning surfaces.
F. SOLDOVIERI +3 more
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Quadratic Convergence in a Primal-Dual Method
Mathematics of Operations Research, 1993We show that the Mizuno-Todd-Ye O(√nL) iteration predictor-corrector primal-dual interior-point algorithm for linear programming is quadratically convergent. Our proof does not assume that the problems be nondegenerate. We do not assume that the iterate generated by the algorithm be convergent, an assumption common to all previous asymptotic ...
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Convergence Properties of the Softassign Quadratic Assignment Algorithm
Neural Computation, 1999The softassign quadratic assignment algorithm is a discrete-time, continuous-state, synchronous updating optimizing neural network. While its effectiveness has been shown in the traveling salesman problem, graph matching, and graph partitioning in thousands of simulations, its convergence properties have not been studied.
A, Rangarajan, A, Vuille, E, Mjolsness
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Accelerating the convergence of the quadratically convergent SCF method
Chemical Physics Letters, 1975Abstract An algorithm to solve the divergence problem of the quadratically convergent SCF method is suggested and is applied to the calculation of the excited states of a number of small molecules. The results show definite improvement over the original method.
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Super-quadratic convergence in Aitken Δ2 process
Japan Journal of Industrial and Applied Mathematics, 2004zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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On global and local convergence of half-quadratic algorithms
Proceedings. International Conference on Image Processing, 2003This paper provides original results on the global and local convergence properties of half-quadratic (HQ) algorithms resulting from the Geman and Yang (GY) and Geman and Reynolds (GR) primal-dual constructions. First, we show that the convergence domain of the GY algorithm can be extended with the benefit of an improved convergence rate.
Allain, Marc +2 more
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Quadratic convergence of an extrapolation method
European Journal of Operational Research, 1993zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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