This book covers linear algebra methods for financial engineering applications from a numerical point of view. The book contains many such applications, as well as pseudocodes, numerical examples, and questions often asked in interviews for quantitative positions. Financial Applications · The Arrow—Debreu one period market model · One period index options arbitrage · Covariance and correlation matrix estimation from time series data · Ordinary least squares for implied volatility computation · Minimum variance portfolios and maximum return portfolios · Value at Risk and portfolio VaR Linear Algebra Topics · LU and Cholesky decompositions and linear solvers · Optimal solvers for tridiagonal symmetric positive matrices · Ordinary least squares and linear regression · Linear Transformation Property · Efficient cubic spline interpolation · Multivariate normal random variables The book is written in a similar spirit as the best selling ``A Primer for the Mathematics of Financial Engineering" by the same author, and should accordingly be useful to a similarly large audience: · Prospective students for financial engineering or mathematical finance programs will be able to self-study material that will prove very important in their future studies · Finance practitioners will find mathematical underpinnings for many methods used in practice, furthering the ability to expand upon these methods · Academics teaching financial engineering courses will be able to use this book as textbook, or as reference book for numerical linear algebra methods with financial applications.