Feb 14, 2019
me, giving a presentation on SVDs: What is an SVD? How do you know when you have one? What are the symptoms?
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me, giving a presentation on SVDs: What is an SVD? How do you know when you have one? What are the symptoms?
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A blog about mathematics.
Finished Chapter 2: Linear Systems of Equations!
20230110
NSGA-II Implementation for Resource Allocation in MIMO-OFDMA
by P. Kanthimathi" NSGA-II Implementation for Resource Allocation in MIMO-OFDMA"Â
Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-5 , August 2018,Â
URL: http://www.ijtsrd.com/papers/ijtsrd15879.pdfÂ
Direct URL: http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/15879/nsga-ii-implementation-for-resource-allocation-in-mimo-ofdma/p-kanthimathi
international journals in engineering, engineering journal, paper publication for engineering
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Get here faster, mail lady, pleaaaase! I want my carbon paper pads so I can get this sweet, sweet matrix flipbook-y gif dinger show on the road! Also, just because I’m not done hyping that article about singular value decomposition yet, here it is again.
Singular Value Decomposition
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We’ve been presented with the definition of the singular value decomposition or SVD, but not how to compute it.
Recall that the definition was
Singular value decomposition (SVD)
Given \( M \in \text{R}^{m \times n} \), we can find a representation of \( M \)
\begin{equation}\label{eqn:multiphysicsL6:81} M = U \Sigma V^\T, \end{…
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ECE1254H Modeling of Multiphysics Systems. Lecture 6: Singular value decomposition, and conditioning number. Taught by Prof. Piero Triverio
ECE1254H Modeling of Multiphysics Systems. Lecture 6: Singular value decomposition, and conditioning number. Taught by Prof. Piero Triverio
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Disclaimer
Peeter’s lecture notes from class. These may be incoherent and rough.
Matrix norm
We’ve defined the matrix norm of \( M \), for the system \( \overline{{y}} = M \overline{{x}} \) as
\begin{equation}\label{eqn:multiphysicsL6:21} \Norm{M} = \max_{\Norm{\overline{{x}}} = 1} \Norm{ M \overline{{x}} }. \end{equation}
We…
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