Advanced Math & Optimization: Numerical Methods, Solvers, and Problem-Solving

By Machine + L.

This book dives into advanced mathematical techniques and optimization strategies, equipping readers with tools to solve complex real-world problems. It bridges theory and computation, emphasizing practical implementations in scientific and engineering contexts.

Key topics include:
- Numerical methods for linear systems: Gaussian elimination, LU decomposition, and iterative solvers like Conjugate Gradient
- Eigenvalue computations and Krylov subspace methods for large-scale problems
- Optimization frameworks: unconstrained/constrained, gradient-based, and stochastic methods
- Linear programming (Simplex, Interior Point) and nonlinear optimization (KKT, SQP)
- Convex optimization theory and applications in machine learning
- Global optimization strategies, including genetic algorithms and simulated annealing
- Parallel/distributed optimization and solver selection for large-scale problems
- Case studies integrating optimization with data science and neural networks

Readers should have prior knowledge of linear algebra (covered in "Mathematical Functions & Linear Algebra in Mojo") and calculus. For applications in dynamical systems, see "Mastering Math with Mojo: Topology, Chaos Theory, and Real-World Projects".

• Format: Paperback
• Language: English
• ISBN: B0FJ6BCTLF
• ISBN13: 9798293105168
• Release Date: July 2025
• Publisher: Independently Published
• Length: 364 Pages
• Weight: 1.07 lbs.
• Dimensions: 0.8" x 6.0" x 9.0"