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Overview Of Linear Dynamical Systems
Linear Functions (Continued)
Linearization (Continued)
Nullspace Of A Matrix (Continued)
Orthonormal Set Of Vectors
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Least-Squares Polynomial Fitting
Multi-Objective Least-Squares Lecture
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Examples Of Autonomous Linear Dynamical Systems Lecture
Solution Via Laplace Transform And Matrix Exponential Lecture
Time Transfer Property Lecture
Markov Chain (Example) Lecture
Jordan Canonical Form Lecture
LU Factorization (Cont.) Lecture
Continue On Unconstrained Minimization Lecture
Newton's Method (Cont.) Lecture

and a lot more..
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An interesting website by Kardi Teknomo. Nice and short tutorials, handy for reference:


k-Means clustering
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Mean and Average
Mean, median, mode
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These are one of the best explained text on various topics by Prof. John H. Mathews


Checkout http://math.fullerton.edu/mathews/index.html for the main page

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by DAVIS BUNDI NTWIGA
Numerical methods form an important part of the pricing of financial derivatives and especially in cases where there is no closed form analytical formula. We begin our work with an introduction of the mathematical tools needed in the pricing of financial derivatives. Then, we discuss the assumption of the log-normal returns on stock prices and the stochastic differential equations. These lay the foundation for the derivation of the Black Scholes differential equation, and various Black Scholes formulas are thus obtained. Then, the model is modified to cater for dividend paying stock and for the pricing of options on futures. Multi-period binomial model is very flexible even for the valuation of options that do not have a closed form analytical formula. We consider the pricing of vanilla options both on non dividend and dividend paying stocks. Then show that the model converges to the Black-Scholes value as we increase the number of steps. We discuss the Finite difference methods quite extensively with a focus on the Implicit and Crank-Nicolson methods, and apply these numerical techniques to the pricing of vanilla options. Finally, we compare the convergence of the binomial model, the Implicit and Crank Nicolson methods to the analytical Black Scholes price of the option

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It seems to be a library of learning material,papers for optimization stuff. You can search from this page, or browse
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A lot of papers on numerical method papers are found on the following topics:

Eigenvalue problems
Least squares problems
Linear systems, condition estimation
Matrix functions and nonlinear matrix equations
Miscellaneous
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Author : Uwe Wystup
Contents
Introduction to Mathematical Finance
Newton's Method and its Application: A VolRetriever. The Excel spreadsheet is here.
Vanilla Options - Properties of the Black-Scholes formula
Volatility Management - Historic versus implied volatility, market data, smile reasons and shapes, risk reversals and butterflies, wing shifts
Normal Distribution and Characteristic Function: Plackett's Identity for Hessian and Correlation - Inversion Theorem by Gil-Paleaz
Efficient Computation of Option Price Sensitivities using Homogeneity and other Tricks
Numerical Integration and its Application to Spread Options: Midpoint Rule, Trapezoidal Rule, Simpson's Rule, Gaussian Quadrature. The Excel spreadsheet is here. There is also C source code and an executable in the front office section.
Fourier Transforms and their application to pricing path-independent multi-asset options, principal component transformation of the Black-Scholes partial differential equation
Binomial Trees in two dimensions and their application to options on the minimum/maximum of two assets. The Excel spreadsheet is here. Further FORTRAN source code is in the front office section
Termstructure Models and their Application, guest lecture by Dr. Ingo Schneider.
The Heat Equation
Finite Difference Methods
Survey on Barrier Options. The presentation on Ensuring Efficient Hedging of Barrier Options is at the section on conference talks.
Exercises

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Prof. Dr. Karsten Urban

Preface 3
1 Introduction 4
2 Numerical Generation of Random Numbers 7
2.1 Congruence Methods . . . . . . . . . . . . . . . . . . . . . . . 8
2.2 Frequency and Gap Tests . . . . . . . . . . . . . . . . . . . . . 10
2.2.1 The 2-Test . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2.2 Gaps . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.3 Discrepancy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.4 Transformed Random Variables . . . . . . . . . . . . . . . . . 15
2.4.1 Inversion . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.4.2 Transformation of Random Variables . . . . . . . . . . 16
2.4.3 Normally Distributed Random Variables . . . . . . . . 18
3 Numerical Cubature and Monte-Carlo Methods 20
3.1 Product Formulas (are useless here) . . . . . . . . . . . . . . . 21
3.2 Monte-Carlo Methods . . . . . . . . . . . . . . . . . . . . . . 23
3.3 Quasi
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Nice notes by Gilles Pag
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by Dr. Thomas Papenbrock
Lecture
1 Introduction: Motivation and course overview
2 Computer use: Security, compiling, linking, graphics
Fortran 90: data types
3 Fortran 90 cont
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Lecture notes : Yue-Kuen KWOK
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Numerical Recipes in C
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Abramowitz and Stegun.
Handbook of Mathematical Functions
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K. Madsen, H.B. Nielsen, O. Tingleff

1. INTRODUCTION AND DEFINITIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
2. DESCENT METHODS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5
2.1. The Steepest Descent method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2. Newton
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834 pages book by J
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