Introduction to Optimization Algorithms

Introduction to Optimization Algorithms
Last updated 1/2026
Duration: 1h 24m | .MP4 1920×1080 30fps(r) | AAC, 44100Hz, 2ch | 361.33 MB

Learn the basic fundamentals of combinatorial and numerical optimization

What you’ll learn
– The concept of P-NP
– The foundations and intuitions of optimization algorithms
– The main algorithms used in combinatorial problems
– You will be able to think computationally regarding many problems

Requirements
– Bssic math knowledge

Description
Course Content:The lecture is structured to move from deterministic methods to stochastic exploration. We begin withOptimization with Derivatives, exploring how the slope of a function guides us to optima. However, as real-world problems often involve highDimensionalityand non-continuous search spaces, we transition intoNumerical Optimization, discussing boundaries, constraints, and the distinction between discrete and continuous problems.

A central theme of the course is the balance betweenExploration and Exploitation. We will analyze howHeuristicsnavigate theSearch Spaceto avoid being trapped inLocal Optima, aiming instead for theGlobal Optimum. The curriculum covers the design ofFitness Functionsand the role ofHyperparametersin tuning algorithm behavior. We also delve into the "intelligence" of nature-inspired methods, such asRandom AlgorithmsandEvolutionary Computing, explaining how these metaheuristics solve problems where traditional derivatives fail.

This course is an essential primer for anyone interested in the mathematical foundations of AI, operations research, and advanced engineering simulation.
Who this course is for:
– People who are interested in optimization algorithms
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