Tired of solving Sudokus by hand? This class teaches you how to solve complex search problems with discrete optimization concepts and algorithms, including constraint programming, local search, and mixed-integer programming.
Optimization technology is ubiquitous in our society. It schedules planes and their crews, coordinates the production of steel, and organizes the transportation of iron ore from the mines to the ports. Optimization clears the day-ahead and real-time markets to deliver electricity to millions of people. It organizes kidney exchanges and cancer treatments and helps scientists understand the fundamental fabric of life, control complex chemical reactions, and design drugs that may benefit billions of individuals.
This class is an introduction to discrete optimization and exposes students to some of the most fundamental concepts and algorithms in the field. It covers constraint programming, local search, and mixed-integer programming from their foundations to their applications for complex practical problems in areas such as scheduling, vehicle routing, supply-chain optimization, and resource allocation.
Дискретная оптимизация
от партнера
Мельбурнский университет
Об этом курсе
4.9
Оценки: 273
Приобретаемые навыки
Constraint ProgrammingBranch And BoundDiscrete OptimizationLinear Programming (LP)
Программа курса: что вы изучите
Неделя
12 ч. на завершение
Welcome
These lectures and readings give you an introduction to this course: its philosophy, organization, and load. They also tell you how the assignments are a significant part of the class. This week covers the common input/output organization of the assignments, how they are graded, and how to succeed in this class.
4 видео ((всего 43 мин.)), 3 материалов для самостоятельного изучения, 1 тест
4 видео
Course Promo1мин
Course Motivation - Indiana Jones, challenges, applications20мин
Course Introduction - philosophy, design, grading rubric11мин
Assignments Introduction & Any Integer9мин
3 материала для самостоятельного изучения
Start of Course Survey10мин
Socialize10мин
Course Syllabus10мин
Неделя
27 ч. на завершение
Knapsack
These lectures introduce optimization problems and some optimization techniques through the knapsack problem, one of the most well-known problem in the field. It discusses how to formalize and model optimization problems using knapsack as an example. It then reviews how to apply dynamic programming and branch and bound to the knapsack problem, providing intuition behind these two fundamental optimization techniques. The concept of relaxation and search are also discussed.
9 видео ((всего 101 мин.)), 1 тест
9 видео
Knapsack 2 - greedy algorithms7мин
Knapsack 3 - modeling8мин
Knapsack 4 - dynamic programming17мин
Knapsack 5 - relaxation, branch and bound14мин
Knapsack 6 - search strategies, depth first, best first, least discrepancy14мин
Assignments Getting Started13мин
Knapsack & External Solver10мин
Exploring the Material - open course design, optimization landscape, picking your adventure10мин
Неделя
317 ч. на завершение
Constraint Programming
Constraint programming is an optimization technique that emerged from the field of artificial intelligence. It is characterized by two key ideas: To express the optimization problem at a high level to reveal its structure and to use constraints to reduce the search space by removing, from the variable domains, values that cannot appear in solutions. These lectures cover constraint programming in detail, describing the language of constraint programming, its underlying computational paradigm and how it can be applied in practice.
13 видео ((всего 248 мин.)), 1 материал для самостоятельного изучения, 2 тестов
13 видео
CP 2 - propagation, arithmetic constraints, send+more=money26мин
CP 3 - reification, element constraint, magic series, stable marriage16мин
CP 4 - global constraint intuition, table constraint, sudoku19мин
CP 5 - symmetry breaking, BIBD, scene allocation18мин
CP 6 - redundant constraints, magic series, market split11мин
CP 7 - car sequencing, dual modeling18мин
CP 8 - global constraints in detail, knapsack, alldifferent33мин
CP 9 - search, first-fail, euler knight, ESDD25мин
CP 10 - value/variable labeling, domain splitting, symmetry breaking in search28мин
Graph Coloring6мин
Optimization Tools5мин
Set Cover8мин
1 материал для самостоятельного изучения
Optimization Tools10мин
Неделя
413 ч. на завершение
Local Search
Local search is probably the oldest and most intuitive optimization technique. It consists in starting from a solution and improving it by performing (typically) local perturbations (often called moves). Local search has evolved substantially in the last decades with a lot of attention being devoted on which moves to explore. These lectures explore the theory and practice of local search, from the concept of neighborhood and connectivity to meta-heuristics such as tabu search and simulated annealing.
10 видео ((всего 191 мин.)), 1 тест
10 видео
LS 2 - swap neighborhood, car sequencing, magic square15мин
LS 3 - optimization, warehouse location, traveling salesman, 2-opt, k-opt23мин
LS 4 - optimality vs feasibility, graph coloring22мин
LS 5 - complex neighborhoods, sports scheduling21мин
LS 6 - escaping local minima, connectivity15мин
LS 7 - formalization, heuristics, meta-heuristics introduction22мин
LS 8 - iterated location search, metropolis heuristic, simulated annealing, tabu search intuition18мин
LS 9 - tabu search formalized, aspiration, car sequencing, n-queens26мин
Traveling Salesman10мин
4.9
Рецензии: 5640%
начал новую карьеру, пройдя эти курсы
38%
получил значимые преимущества в карьере благодаря этому курсу
Лучшие рецензии
I like the instructor teaching approach and the evaluation system, the subject itself took me a lot of effort and i think the LNS technique should be teached just after local search.
i wish there was 6 start rating so i can give this prof his due, he made a very complicated subject look very simple and easy to understand thanks a million
Преподаватели
Dr. Carleton Coffrin
Adjunct LecturerComputing and Information Systems
О Мельбурнский университет
The University of Melbourne is an internationally recognised research intensive University with a strong tradition of excellence in teaching, research, and community engagement. Established in 1853, it is Australia's second oldest University.
Часто задаваемые вопросы
Когда я получу доступ к лекциям и заданиям?
Зарегистрировавшись на сертификацию, вы получите доступ ко всем видео, тестам и заданиям по программированию (если они предусмотрены). Задания по взаимной оценке сокурсниками можно сдавать и проверять только после начала сессии. Если вы проходите курс без оплаты, некоторые задания могут быть недоступны.
Что я получу, оплатив сертификацию?
Оплатив сертификацию, вы получите доступ ко всем материалам курса, включая оцениваемые задания. После успешного прохождения курса на странице ваших достижений появится электронный сертификат. Оттуда его можно распечатать или прикрепить к профилю LinkedIn. Просто ознакомиться с содержанием курса можно бесплатно.
Какие правила возврата средств?
Можно ли получить финансовую помощь?
• What are the pre-requisites for the class?
Good programming skills, knowledge of algorithms and linear algebra.
• What programming language will be used in this class?
A minimal knowledge of python is necessary to integrate with the course infrastructure. Outside of that, students are free to use any language of their choice.
• How difficult is this class?
A motivated student spending the time on the programming assignment will succeed in this class.
• Where can I get one of those T-Shirts?
At the discrete optimization store: http://www.zazzle.com.au/discreteoptimization
Остались вопросы? Посетите Центр поддержки учащихся.
Coursera делает лучшее в мире образование доступным каждому, предлагая онлайн-курсы от ведущих университетов и организаций.
© Coursera Inc., 2019 Все права защищены.
