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.
Otimização discreta
oferecido por
Universidade de Melbourne
Informações sobre o curso
4.9
273 classificações
Habilidades que você terá
Constraint ProgrammingBranch And BoundDiscrete OptimizationLinear Programming (LP)
Programa - O que você aprenderá com este curso
Semana
12 horas para concluir
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 vídeos (total de (Total 43 mín.) min), 3 leituras, 1 teste
4 videos
Course Promo1min
Course Motivation - Indiana Jones, challenges, applications20min
Course Introduction - philosophy, design, grading rubric11min
Assignments Introduction & Any Integer9min
3 leituras
Start of Course Survey10min
Socialize10min
Course Syllabus10min
Semana
27 horas para concluir
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 vídeos (total de (Total 101 mín.) min), 1 teste
9 videos
Knapsack 2 - greedy algorithms7min
Knapsack 3 - modeling8min
Knapsack 4 - dynamic programming17min
Knapsack 5 - relaxation, branch and bound14min
Knapsack 6 - search strategies, depth first, best first, least discrepancy14min
Assignments Getting Started13min
Knapsack & External Solver10min
Exploring the Material - open course design, optimization landscape, picking your adventure10min
Semana
317 horas para concluir
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 vídeos (total de (Total 248 mín.) min), 1 leitura, 2 testes
13 videos
CP 2 - propagation, arithmetic constraints, send+more=money26min
CP 3 - reification, element constraint, magic series, stable marriage16min
CP 4 - global constraint intuition, table constraint, sudoku19min
CP 5 - symmetry breaking, BIBD, scene allocation18min
CP 6 - redundant constraints, magic series, market split11min
CP 7 - car sequencing, dual modeling18min
CP 8 - global constraints in detail, knapsack, alldifferent33min
CP 9 - search, first-fail, euler knight, ESDD25min
CP 10 - value/variable labeling, domain splitting, symmetry breaking in search28min
Graph Coloring6min
Optimization Tools5min
Set Cover8min
1 leituras
Optimization Tools10min
Semana
413 horas para concluir
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 vídeos (total de (Total 191 mín.) min), 1 teste
10 videos
LS 2 - swap neighborhood, car sequencing, magic square15min
LS 3 - optimization, warehouse location, traveling salesman, 2-opt, k-opt23min
LS 4 - optimality vs feasibility, graph coloring22min
LS 5 - complex neighborhoods, sports scheduling21min
LS 6 - escaping local minima, connectivity15min
LS 7 - formalization, heuristics, meta-heuristics introduction22min
LS 8 - iterated location search, metropolis heuristic, simulated annealing, tabu search intuition18min
LS 9 - tabu search formalized, aspiration, car sequencing, n-queens26min
Traveling Salesman10min
40%
comecei uma nova carreira após concluir estes cursos
38%
consegui um benefício significativo de carreira com este curso
Melhores avaliações
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
Instrutores
Dr. Carleton Coffrin
Adjunct LecturerComputing and Information Systems
Sobre Universidade de Melbourne
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.
Perguntas Frequentes – FAQ
Quando terei acesso às palestras e às tarefas?
Ao se inscrever para um Certificado, você terá acesso a todos os vídeos, testes e tarefas de programação (se aplicável). Tarefas avaliadas pelos colegas apenas podem ser enviadas e avaliadas após o início da sessão. Caso escolha explorar o curso sem adquiri-lo, talvez você não consiga acessar certas tarefas.
O que recebo ao adquirir o Certificado?
Quando você adquire o Certificado, ganha acesso a todo o material do curso, incluindo avaliações com nota atribuída. Após concluir o curso, seu Certificado eletrônico será adicionado à sua página de Participações e você poderá imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, você poderá frequentá-lo como ouvinte sem custo.
Qual é a política de reembolso?
Existe algum auxílio financeiro disponível?
• 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
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