Optimization Algorithms: Applications with Python, Java

Learn optimization algorithms with practical Python and Java projects, from linear programming to heuristics

What you'll learn
- Formulate optimization problems and solve them using linear programming and the simplex method.
- Apply optimization techniques to real-world cases such as vehicle routing, production planning, and resource allocation.
- Implement optimization algorithms in Python and Java, and understand when to use each language in practice.
- Use heuristic methods like genetic algorithms and simulated annealing to solve complex problems where exact solutions are not practical.

Requirements
- A basic understanding of Java programming is required, since part of the course uses Java for optimization projects.
- Familiarity with Python is helpful but not mandatory.
- No prior knowledge of optimization is needed. All mathematical and algorithmic concepts will be introduced during the course.
- A computer with Python and Java installed

Description
This course is designed for anyone who wants to learn optimization algorithms and apply them directly to real projects. Optimization is at the heart of decision-making in production, logistics, scheduling, and many areas of data-driven business. In this course, we’ll not only cover the mathematical foundations but also show how to implement the algorithms in two of the most widely used programming languages in industry: Python and Java.

Why Python and Java? In practice, Python is often used for prototyping, data analysis, and building optimization models quickly thanks to its large ecosystem of libraries. Java, on the other hand, is still widely adopted in enterprise systems, especially when optimization needs to be integrated into larger applications. By learning both, you’ll be better prepared for what companies actually use in real-life projects.

The course begins with the basics: linear programming, simplex method, and how to formulate problems correctly. From there, we move on to applied cases such as vehicle routing, production planning, and Bayesian optimization. You’ll also work with heuristic methods like genetic algorithms and simulated annealing, learning how they provide solutions when exact methods are not feasible.

All the lectures come with coding examples, step-by-step explanations, and clear connections between theory and application. By the end of the course, you will have a solid understanding of optimization algorithms, the ability to code them in Python and Java, and the confidence to apply them in your own projects or professional work.

Who this course is for:
- Students and professionals who want to learn optimization algorithms and apply them in Python and Java.
- Java developers who want to expand their skills into mathematical optimization and algorithmic problem solving.
- Data analysts, engineers, and researchers looking to apply optimization in logistics, scheduling, manufacturing, or business decision-making.
- Learners who want a practical balance of theory and implementation across two widely used programming languages.
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