https://mklab.gr/ Wed, 29 Jul 2026 13:59:55 +0000 MyBB https://mklab.gr/showthread.php?tid=639 Tue, 23 Jun 2026 10:07:51 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=639  Computational Optimization Open Textbook
BY  Cornell University

Summary

The Cornell University Computational Optimization Open Textbook is an open-source educational resource that introduces the ideas, methods, and real-world applications of optimization—the science of finding the best possible solution under given conditions. 
Created through collaboration between Professor Fengqi You, teaching assistants, and students, the textbook covers a wide range of topics including linear programming, nonlinear optimization, mixed-integer problems, global optimization, dynamic programming, optimization under uncertainty, and modern methods used in machine learning such as Adam and stochastic gradient descent. 
It is designed to make complex mathematical optimization techniques more accessible by combining theory, algorithms, examples, and applications from engineering, computer science, and data analytics. 

BOOK]]>  Computational Optimization Open Textbook
BY  Cornell University

Summary

The Cornell University Computational Optimization Open Textbook is an open-source educational resource that introduces the ideas, methods, and real-world applications of optimization—the science of finding the best possible solution under given conditions. 
Created through collaboration between Professor Fengqi You, teaching assistants, and students, the textbook covers a wide range of topics including linear programming, nonlinear optimization, mixed-integer problems, global optimization, dynamic programming, optimization under uncertainty, and modern methods used in machine learning such as Adam and stochastic gradient descent. 
It is designed to make complex mathematical optimization techniques more accessible by combining theory, algorithms, examples, and applications from engineering, computer science, and data analytics. 

BOOK]]> https://mklab.gr/showthread.php?tid=391 Tue, 16 Jun 2026 03:08:41 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=391 EE 227C Convex Optimization and Approximation

Summary

The EE227C course site contains graduate-level course notes and materials on convex optimization and approximation, focusing on how optimization theory is used in machine learning and modern data analysis. It presents a structured introduction to continuous optimization methods, including gradient-based algorithms, stochastic optimization, duality, non-convex optimization, and higher-order methods, while also emphasizing computational efficiency, robustness, and practical implementation. 
The materials are organized as lecture-style notes paired with Python/Jupyter notebooks that illustrate theoretical concepts through experiments. Overall, the course aims to bridge rigorous mathematical optimization theory with real-world applications in data science, showing how modern algorithms for learning and large-scale optimization can be derived, analyzed, and applied in practice.

COURSE PAGE]]> EE 227C Convex Optimization and Approximation

Summary

The EE227C course site contains graduate-level course notes and materials on convex optimization and approximation, focusing on how optimization theory is used in machine learning and modern data analysis. It presents a structured introduction to continuous optimization methods, including gradient-based algorithms, stochastic optimization, duality, non-convex optimization, and higher-order methods, while also emphasizing computational efficiency, robustness, and practical implementation. 
The materials are organized as lecture-style notes paired with Python/Jupyter notebooks that illustrate theoretical concepts through experiments. Overall, the course aims to bridge rigorous mathematical optimization theory with real-world applications in data science, showing how modern algorithms for learning and large-scale optimization can be derived, analyzed, and applied in practice.

COURSE PAGE]]> https://mklab.gr/showthread.php?tid=350 Sun, 14 Jun 2026 16:10:36 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=350 Lectures on Modern Convex Optimizaion 
by Arkadii Nemirovski

Summary  Lectures on Modern Convex Optimization: Analysis, Algorithms, and Engineering Applications presents and analyzes numerous engineering models, illustrating the wide spectrum of potential applications of the new theoretical and algorithmical techniques emerging from the significant progress taking place in convex optimization. It is hoped that the information provided here will serve to promote the use of these techniques in engineering practice. The book develops a kind of “algorithmic calculus” of convex problems, which can be posed as conic quadratic and semidefinite programs. This calculus can be viewed as a “computationally tractable” version of the standard convex analysis.

BOOK PAGE]]> Lectures on Modern Convex Optimizaion 
by Arkadii Nemirovski

Summary  Lectures on Modern Convex Optimization: Analysis, Algorithms, and Engineering Applications presents and analyzes numerous engineering models, illustrating the wide spectrum of potential applications of the new theoretical and algorithmical techniques emerging from the significant progress taking place in convex optimization. It is hoped that the information provided here will serve to promote the use of these techniques in engineering practice. The book develops a kind of “algorithmic calculus” of convex problems, which can be posed as conic quadratic and semidefinite programs. This calculus can be viewed as a “computationally tractable” version of the standard convex analysis.

BOOK PAGE]]> https://mklab.gr/showthread.php?tid=349 Sun, 14 Jun 2026 16:07:49 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=349 Algorithms for optimization
by Mykel Kochenderfer, Tim Wheeler

Summary  Algorithms for Optimization (published by MIT Press) by Mykel J. Kochenderfer and Tim A. Wheeler is a comprehensive guide to practical optimization algorithms for engineering and design. It provides concrete implementations of various computational techniques using the Julia programming language

BOOK PAGE]]> Algorithms for optimization
by Mykel Kochenderfer, Tim Wheeler

Summary  Algorithms for Optimization (published by MIT Press) by Mykel J. Kochenderfer and Tim A. Wheeler is a comprehensive guide to practical optimization algorithms for engineering and design. It provides concrete implementations of various computational techniques using the Julia programming language

BOOK PAGE]]> https://mklab.gr/showthread.php?tid=321 Sat, 13 Jun 2026 13:26:56 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=321 Lecture Notes on Optimization 
Pravin Varaiya

Summary

These Notes were developed for a ten-week course I have taught for the past three years to first-year graduate students of the University of California at Berkeley. My objective has been to present, in a compact and unified manner, the main concepts and techniques of mathematical programming and optimal control to students having diverse technical backgrounds. A reasonable knowledge of advanced calculus (up to the Implicit Function Theorem), linear algebra (linear independence, basis, matrix inverse), and linear differential equations (transition matrix, adjoint solution) is sufficient for the reader to follow the Notes.

BOOK PAGE]]> Lecture Notes on Optimization 
Pravin Varaiya

Summary

These Notes were developed for a ten-week course I have taught for the past three years to first-year graduate students of the University of California at Berkeley. My objective has been to present, in a compact and unified manner, the main concepts and techniques of mathematical programming and optimal control to students having diverse technical backgrounds. A reasonable knowledge of advanced calculus (up to the Implicit Function Theorem), linear algebra (linear independence, basis, matrix inverse), and linear differential equations (transition matrix, adjoint solution) is sufficient for the reader to follow the Notes.

BOOK PAGE]]> https://mklab.gr/showthread.php?tid=258 Wed, 10 Jun 2026 04:06:50 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=258


Convex Optimization
by Stephen Boyd

Summary  Convex optimization problems arise frequently in many different fields. A comprehensive introduction to the subject, this book shows in detail how such problems can be solved numerically with great efficiency. The focus is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. The text contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance, and economics.

BOOK PAGE

The slides are provided here --->SLIDES ]]>


Convex Optimization
by Stephen Boyd

Summary  Convex optimization problems arise frequently in many different fields. A comprehensive introduction to the subject, this book shows in detail how such problems can be solved numerically with great efficiency. The focus is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. The text contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance, and economics.

BOOK PAGE

The slides are provided here --->SLIDES ]]>