Optimization: principles and algorithms - Linear optimization
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Brief Introduction
Introduction to linear optimization, duality and the simplex algorithm.
Description
Introduction to linear optimization, duality and the simplex algorithm.
Knowledge
- The course is structured into 5 sections.
- Formulation: you will learn from simple examples how to formulate, transform and characterize an optimization problem.
- Constraints: you will learn how to represent the constraints of a linear optimization problem, both from a geometric and algebraic point of views.
- Duality: you will learn how to derive a companion problem called the "dual".
- Optimality conditions: you will learn sufficient and necessary conditions for an optimal solution.
- Simplex method: you will learn an algorithm to solve a linear optimization problem.
Machine Learning and AI: Support Vector Machines in Python
- 4.6
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Artificial Intelligence and Data Science Algorithms in Python for Classification and Regression The toughest obstacle to overcome when you’re learning about support vector machines is that they are very theoretical....
Algorithmic Toolbox
- 4.6
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The course covers basic algorithmic techniques and ideas for computational problems arising frequently in practical applications: sorting and searching, divide and conquer, greedy algorithms, dynamic programming....
Shortest Paths Revisited, NP-Complete Problems and What To Do About Them
- 4.8
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Week 1 Single-Source Shortest Paths, Revisted Optimal Substructure The Basic Algorithm I The Basic Algorithm II Detecting Negative Cycles A Space Optimization...
Convex Optimization
- 0.0
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This course concentrates on recognizing and solving convex optimization problems that arise in applications. This course concentrates on recognizing and solving convex optimization problems that arise in applications....
