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Computation and Control | A blog on control theory and its applications | jleny.wordpress.com Reviews
https://jleny.wordpress.com
A blog on control theory and its applications
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Convergence of Stochastic Gradient Algorithms | Computation and Control
https://jleny.wordpress.com/2011/11/27/convergence-of-stochastic-gradient-algorithms
A blog on control theory and its applications. Convergence of Stochastic Gradient Algorithms. November 27, 2011 in algorithms. I use stochastic gradient and stochastic approximation algorithms regularly, and I often find it surprisingly difficult to locate basic results on the convergence of these algorithms, despite multiple books written on the subject. For example, consider a convex function. Denote the set of minima of. Is not empty, then we know that it is convex set, but we do not know a priori if.
DARPA META Program | Computation and Control
https://jleny.wordpress.com/2010/08/09/darpa-meta-program
A blog on control theory and its applications. August 9, 2010 in Cyber-Physical Systems. DARPA has an interesting program called META. Update 11/02/10: Aviation week has another interesting article. On systems engineering and integration, which mentions the META program. Control Theory and Optimization. History of Feedback Control. Comments feed for this article. Leave a Reply Cancel reply. Enter your comment here. Please log in using one of these methods to post your comment:. Address never made public).
Bonmin and Ipopt Compilation on Mac OS 10.6 | Computation and Control
https://jleny.wordpress.com/2010/06/29/bonmin-and-ipopt-compilation-on-mac-os-10-6
A blog on control theory and its applications. Bonmin and Ipopt Compilation on Mac OS 10.6. June 29, 2010 in Optimization. Is a solver for mixed-integer nonlinear programs. It is a global solver if the continuous relaxation is a convex program, and my hope is that it will perform better than the naive branch-and-bound I recently had to implement for a project (written in Python with cvxopt. For quick development reasons, its performance was about the same as Yalmip bnb. Control Theory and Optimization.
Installing the GLFW package for Haskell | Computation and Control
https://jleny.wordpress.com/2011/11/06/installing-the-glfw-package-for-haskell
A blog on control theory and its applications. Installing the GLFW package for Haskell. November 6, 2011 in Programming. Cabal cannot install the GLFW. Package version 0.4.2 for GHC 7.0.3, at least on Mac OS X 10.6. 64-bit. This seems to be a well-know bug but I couldn’t find a solution online. To fix the problem, you can install the GLFW. Library directly, then do. Sudo cabal install glfw -fdynamic. The “dynamic” flag avoids that cabal tries to recompile its version of GLFW. History of Feedback Control.
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infostructuralist.wordpress.com
April | 2015 | The Information Structuralist
https://infostructuralist.wordpress.com/2015/04
Skip to search - Accesskey = s. Counting bits with Vapnik and Chervonenkis. Posted in Information Theory. Statistical Learning and Inference. By mraginsky on April 28, 2015. Machine learning is about enabling computers to improve their performance on a given task as they get more data. Can we express this intuition quantitatively using information-theoretic techniques? In this post, I will discuss a classic paper. Models of Complex Stochastic Systems. Statistical Learning and Inference.
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Information flow on graphs | The Information Structuralist
https://infostructuralist.wordpress.com/2014/05/03/information-flow-on-graphs
Skip to search - Accesskey = s. Information flow on graphs. Posted in Information Theory. Models of Complex Stochastic Systems. By mraginsky on May 3, 2014. In a very nice paper. Which deserves to be more widely known. The system Dawson analyzed is an instance of a Probabilistic Cellular Automaton. Is determined stochastically by its own state, as well as by the states of its neighbors, at time. Now let’s move on to precise definitions. Consider an undirected graph. Is the set of all vertices. We allow a...
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Lossless source coding at Western Union | The Information Structuralist
https://infostructuralist.wordpress.com/2013/09/19/lossless-source-coding-at-western-union
Skip to search - Accesskey = s. Lossless source coding at Western Union. Posted in Information Theory. By mraginsky on September 19, 2013. 8230; circa 1935. Here is a quote from. An American travelogue by Ilya Ilf and Yevgeny Petrov. Typical set encoding, anyone? Laquo; ISIT 2013: two plenaries on concentration of measure. A graph-theoretic derivation of the Gilbert-Varshamov bound. Leave a Reply Cancel reply. Enter your comment here. Fill in your details below or click an icon to log in:.
infostructuralist.wordpress.com
Counting bits with Vapnik and Chervonenkis | The Information Structuralist
https://infostructuralist.wordpress.com/2015/04/28/counting-bits-with-vapnik-and-chervonenkis
Skip to search - Accesskey = s. Counting bits with Vapnik and Chervonenkis. Posted in Information Theory. Statistical Learning and Inference. By mraginsky on April 28, 2015. Machine learning is about enabling computers to improve their performance on a given task as they get more data. Can we express this intuition quantitatively using information-theoretic techniques? In this post, I will discuss a classic paper. We will stick to the setting of binary classification: we have a feature space. Is a sequen...
infostructuralist.wordpress.com
ISIT 2013: two plenaries on concentration of measure | The Information Structuralist
https://infostructuralist.wordpress.com/2013/07/29/isit-2013-two-plenaries-on-concentration-of-measure
Skip to search - Accesskey = s. ISIT 2013: two plenaries on concentration of measure. Posted in Conference Blogging. By mraginsky on July 29, 2013. Of the five plenary talks at this year’s ISIT. Two were about concentration of measure: Katalin Marton’s Shannon lecture on “Distance-divergence inequalities” and Gabor Lugosi’s talk on “Concentration inequalities and the entropy method” the next morning. Since the topic of measure concentration is dear to my heart. Then we say that. Another way to express (1).
infostructuralist.wordpress.com
Conditional mutual information and the best Markov approximation | The Information Structuralist
https://infostructuralist.wordpress.com/2013/01/04/conditional-mutual-information-and-the-best-markov-approximation
Skip to search - Accesskey = s. Conditional mutual information and the best Markov approximation. Posted in Information Theory. By mraginsky on January 4, 2013. I came across a neat and useful result about conditional mutual information while reading a paper on quantum information theory. Consider three jointly distributed random variables. If the conditional mutual information. Are conditionally independent given. Is a Markov chain). In fact,. If and only if. Now, what if. Is nonzero, but very small?
infostructuralist.wordpress.com
Two public service announcements | The Information Structuralist
https://infostructuralist.wordpress.com/2014/02/25/two-public-service-announcements
Skip to search - Accesskey = s. Two public service announcements. Posted in Information Theory. By mraginsky on February 25, 2014. 1 The 2014 IEEE North American Summer School on Information Theory. Will take place June 18-21, 2014 at the Fields Institute in Toronto, Canada. 2 For those of you who use Matlab or Octave, there is a new Information Theoretical Estimators (ITE) toolbox. By the toolbox’s creator, Zoltán Zsabó. At the Princeton Information Theory b-log. Information flow on graphs. Enter your e...
infostructuralist.wordpress.com
A graph-theoretic derivation of the Gilbert-Varshamov bound | The Information Structuralist
https://infostructuralist.wordpress.com/2013/09/23/a-graph-theoretic-derivation-of-the-gilbert-varshamov-bound
Skip to search - Accesskey = s. A graph-theoretic derivation of the Gilbert-Varshamov bound. Posted in Coding Theory. By mraginsky on September 23, 2013. Just a quick note for my reference, but it may be of interest to others. Denote the size of the largest code over a. Ary alphabet that has blocklength. The well-known Gilbert-Varshamov bound. Is the volume of a Hamming ball of radius. The usual way of arriving at the GV bound is through a greedy construction: pick an arbitrary codeword. If no two vertic...
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It’s for a good cause! | The Information Structuralist
https://infostructuralist.wordpress.com/2013/05/02/its-for-a-good-cause
Skip to search - Accesskey = s. It’s for a good cause! Posted in Information Theory. By mraginsky on May 2, 2013. To honor Claude Elwood Shannon with a United States Postal Service stamp on the 100th anniversary of his birth. Laquo; Stochastic kernels vs. conditional probability distributions. ISIT 2013: two plenaries on concentration of measure. Leave a Reply Cancel reply. Enter your comment here. Fill in your details below or click an icon to log in:. Address never made public). Follow Blog via Email.
infostructuralist.wordpress.com
Stochastic kernels vs. conditional probability distributions | The Information Structuralist
https://infostructuralist.wordpress.com/2013/03/17/stochastic-kernels-vs-conditional-probability-distributions
Skip to search - Accesskey = s. Stochastic kernels vs. conditional probability distributions. By mraginsky on March 17, 2013. 8216;s recent post. About misinterpretation of p-values is a good reminder about a fundamental distinction anyone working in information theory, control or machine learning should be aware of — namely, the distinction between stochastic kernels and conditional probability distributions. Roughly speaking, stochastic kernels are building blocks, objects that have to be interconnected.
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Computation and Control | A blog on control theory and its applications
A blog on control theory and its applications. Convergence of Stochastic Gradient Algorithms. November 27, 2011 in algorithms. I use stochastic gradient and stochastic approximation algorithms regularly, and I often find it surprisingly difficult to locate basic results on the convergence of these algorithms, despite multiple books written on the subject. For example, consider a convex function. Denote the set of minima of. Is not empty, then we know that it is convex set, but we do not know a priori if.
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