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Graphical Analysis of Prism Adaptation, Convergence Accommodation, and Accommodative Convergence
Optometry and Vision Science, 1982A new form of graphical case analysis is described which quantifies static interactions between accommodative convergence, convergence accommodation, and prism adaptation. A clinical gradient measure of the CA/C ratio is compared to haploscopic measures to demonstrate the validity of the new clinical technique for measuring convergence accommodation ...
C M, Schor, V, Narayan
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Globally convergent autocalibration using interval analysis [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004 We address the problem of autocalibration of a moving camera with unknown constant intrinsic parameters. Existing autocalibration techniques use numerical optimization algorithms whose convergence to the correct result cannot be guaranteed, in general.
FUSIELLO, Andrea +3 more
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2004
Proving convergence of the various optimization algorithms is a delicate exercise. In general, it is helpful to consider local and global convergence patterns separately. The local convergence rate of an algorithm provides a useful benchmark for comparing it to other algorithms. On this basis, Newton’s method wins hands down. However, the tradeoffs are
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Proving convergence of the various optimization algorithms is a delicate exercise. In general, it is helpful to consider local and global convergence patterns separately. The local convergence rate of an algorithm provides a useful benchmark for comparing it to other algorithms. On this basis, Newton’s method wins hands down. However, the tradeoffs are
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2008
In this chapter we begin our formal analysis of the stochastic approximation scheme in ℛ d given by $${x_{n + 1}} = {x_n} + a\left( n \right)\left[ {h\left( {{x_n}} \right) + {M_{n + 1}}} \right],n \geqslant 0$$ (2.1.1) , with prescribed x0 and with the following assumptions which we recall from the last chapter: (A1) The map h : ℛ d →
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In this chapter we begin our formal analysis of the stochastic approximation scheme in ℛ d given by $${x_{n + 1}} = {x_n} + a\left( n \right)\left[ {h\left( {{x_n}} \right) + {M_{n + 1}}} \right],n \geqslant 0$$ (2.1.1) , with prescribed x0 and with the following assumptions which we recall from the last chapter: (A1) The map h : ℛ d →
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2020
One of the major issues that many practitioners run into when using MCMC is the slow convergence rate. While many MCMC methods have been shown to converge to the target distribution, the entire convergence largely depends upon the magnitude of the second largest eigenvalue of the transition matrix λslem.
Adrian Barbu, Song-Chun Zhu
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One of the major issues that many practitioners run into when using MCMC is the slow convergence rate. While many MCMC methods have been shown to converge to the target distribution, the entire convergence largely depends upon the magnitude of the second largest eigenvalue of the transition matrix λslem.
Adrian Barbu, Song-Chun Zhu
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Local convergence analysis for Chebyshev’s method
Journal of Applied Mathematics and Computing, 2018zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kumari, Chandni, Parida, P. K.
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Analysis Convergence Guidelines
2019The introduction chapter has provided a main focus on the methodology to minimize the risk of troubleshooting during analysis. A quality mindset and philosophy have been also given as a recommended way of working and as a good practice to follow in order to conduct an analysis properly.
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Convergence analysis of the ChebFilterCG algorithm
Numerical Linear Algebra with Applications, 2017SummaryThe ChebFilterCG algorithm, proposed by Golub, Ruiz, and Touhami [SIAM J. Matrix Anal. Appl. 29 (2007), pp. 774‐795] is an iterative method that combines Chebyshev filter and conjugate gradient for solving symmetric positive definite linear systems with multiple right‐hand sides.
Sadkane, Miloud, Touhami, Ahmed
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Convergence Analysis for Three Parareal Solvers
SIAM Journal on Scientific Computing, 2015Summary: We analyze in this paper the convergence properties of the parareal algorithm for the symmetric positive definite problem \(\mathbf{u}'+A\mathbf{u}=g\). The coarse propagator \(\mathcal{G}\) is fixed to the backward-Euler method and three time integrators are chosen for the \(\mathcal{F}\)-propagator: the trapezoidal rule, the third-order ...
Wu, Shu-Lin, Zhou, Tao
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Feedback control analysis of accommodative convergence
The American Journal of Surgery, 1967Abstract We have attempted to show how, in the analysis of complex systems, the parallel paths taken by both engineering and biology have begun to converge. The biologist has provided models and insights for the engineer whereas the engineer has provided new tools and the mathematical formalisms of systems analysis. An example is provided to show how
J, Brodkey, L, Stark
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