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[A:4] Matrix decomposition for regression analysis. D. Bates. 2007 (http://www.stat.wisc.edu/courses/st849-bates/lectures/Orthogonal.pdf)

[A:5] Eigenvalues and Eigenvectors of Symmetric Matrices. I. Mateev. 2013 (http://www.slideshare.net/vanchizzle/eigenvalues-and-eigenvectors-of-symmetric-matrices)

[A:6] Linear Algebra Done Right 2nd Edition (§5 Eigenvalues and Eigenvectors) S Axler. Springer. 2000

[A:7] First Order Predicate Logic. S. Kaushik. CSE India Institute of Technology, Delhi (http://www.cse.iitd.ac.in/~saroj/LFP/LFP_2013/L4.pdf)

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[A:9] Gradient descent. Wikipedia (http://en.wikipedia.org/wiki/Gradient_descent)

[A:10] Large Scale Machine Learning: Stochastic Gradient Descent Convergence. A. Ng. Stanford University (https://class.coursera.org/ml-003/lecture/107)

[A:11] Large-Scala Machine Learning with Stochastic Gradient Descent. L Bottou. 2010 (http://leon.bottou.org/publications/pdf/compstat-2010.pdf)

[A:12] Overview of Quasi-Newton optimization methods. Dept. Computer Science, University of Washington (https://homes.cs.washington.edu/~galen/files/quasi-newton-notes.pdf)

[A:13] Lecture 2-3: Gradient and Hessian of Multivariate Function. M. Zibulevsky. 2013 (http://www.youtube.com)

[A:14] Introduction to the Lagrange Multiplier. ediwm.com video (http://www.noodle.com/learn/details/334954/introduction-to-the-lagrange-multiplier)

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[A:21] List of financial data feeds. Wikipedia (http://en.wikipedia.org/wiki/List_of_financial_data_feeds)