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1. Introduction; Part I. Linear Algebra: 2. Vectors; 3. Matrices; 4. Symmetric matrices; 5. Singular value decomposition; 6. Linear equations and least-squares; 7. Matrix algorithms; Part II. Convex Optimization: 8. Convexity; 9. Linear, quadratic and geometric models; 10. Second-order cone and robust models; 11. Semidefinite models; 12. Introduction to algorithms; Part III. Applications: 13. Learning from data; 14. Computational finance; 15. Control problems; 16. Engineering design.

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