Optimization | Professional literature for training, studies and practice

In the Optimization category, you will find specialized literature on mathematical methods and algorithms for solving optimization problems in theory and practice. The works cover topics such as linear and nonlinear optimization, global and parametric approaches, stochastic methods, and applications in machine learning, robotics, mathematical economics, and engineering. Typical types of works include textbooks, introductions, handbooks, and application-oriented presentations with examples, exercises, or implementations in Python, MATLAB, or Scilab.

The target audience includes students, researchers, and professionals in mathematics, computer science, engineering, economics, and data science who deal with optimization models, algorithms, or practical solutions for complex decision-making processes. The titles cover both fundamental concepts and current developments such as evolutionary algorithms, reinforcement learning, and hybrid systems.