959 resultados para CONVEX HYPERSURFACES
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2000 Mathematics Subject Classification: 90C48, 49N15, 90C25
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2000 Mathematics Subject Classification: Primary 90C29; Secondary 49K30.
An efficient, approximate path-following algorithm for elastic net based nonlinear spike enhancement
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Unwanted spike noise in a digital signal is a common problem in digital filtering. However, sometimes the spikes are wanted and other, superimposed, signals are unwanted, and linear, time invariant (LTI) filtering is ineffective because the spikes are wideband - overlapping with independent noise in the frequency domain. So, no LTI filter can separate them, necessitating nonlinear filtering. However, there are applications in which the noise includes drift or smooth signals for which LTI filters are ideal. We describe a nonlinear filter formulated as the solution to an elastic net regularization problem, which attenuates band-limited signals and independent noise, while enhancing superimposed spikes. Making use of known analytic solutions a novel, approximate path-following algorithm is given that provides a good, filtered output with reduced computational effort by comparison to standard convex optimization methods. Accurate performance is shown on real, noisy electrophysiological recordings of neural spikes.
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Николай М. Николов - Разгледани са характеризации на различни понятия за изпъкналост, като тези понятия са сравнени.
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AMS subject classification: 90B80.
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AMS subject classification: 90C30, 90C33.
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AMS subject classification: Primary 49J52; secondary: 26A27, 90C48, 47N10.
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2010 Mathematics Subject Classification: 35A23, 35B51, 35J96, 35P30, 47J20, 52A40.
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2000 Mathematics Subject Classification: 46B28, 47D15.
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2000 Mathematics Subject Classification: Primary 46H05, 46H20; Secondary 46M20.
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2000 Mathematics Subject Classification: 30C25, 30C45.
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2000 Mathematics Subject Classification: 26E25, 41A35, 41A36, 47H04, 54C65.
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In this paper, we give several results for majorized matrices by using continuous convex function and Green function. We obtain mean value theorems for majorized matrices and also give corresponding Cauchy means, as well as prove that these means are monotonic. We prove positive semi-definiteness of matrices generated by differences deduced from majorized matrices which implies exponential convexity and log-convexity of these differences and also obtain Lypunov's and Dresher's type inequalities for these differences.
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2000 Mathematics Subject Classification: 90C46, 90C26, 26B25, 49J52.
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2000 Mathematics Subject Classification: 26E35, 14H05, 14H20.