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.
Optimisation discrète
Université de MelbourneÀ propos de ce cours
74,279 consultations récentes
Résultats de carrière des étudiants
33%
ont commencé une nouvelle carrière après avoir terminé ce cours
21%
ont bénéficié d'un avantage concret dans leur carrières grâce à ce cours
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Niveau intermédiaire
Approx. 65 heures pour terminer
Anglais
Sous-titres : Français, Portugais (brésilien), Russe, Anglais, Espagnol
Compétences que vous acquerrez
Constraint ProgrammingBranch And BoundDiscrete OptimizationLinear Programming (LP)
Résultats de carrière des étudiants
33%
ont commencé une nouvelle carrière après avoir terminé ce cours
21%
ont bénéficié d'un avantage concret dans leur carrières grâce à ce cours
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Niveau intermédiaire
Approx. 65 heures pour terminer
Anglais
Sous-titres : Français, Portugais (brésilien), Russe, Anglais, Espagnol
Offert par
Université 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.
Programme du cours : ce que vous apprendrez dans ce cours
2 heures pour terminer
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.
2 heures pour terminer
4 vidéos (Total 43 min), 3 lectures, 1 quiz
4 vidéos
Course Promo1 min
Course Motivation - Indiana Jones, challenges, applications20 min
Course Introduction - philosophy, design, grading rubric11 min
Assignments Introduction & Any Integer9 min
3 lectures
Start of Course Survey10 min
Socialize10 min
Course Syllabus10 min
7 heures pour terminer
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.
7 heures pour terminer
9 vidéos (Total 101 min)
9 vidéos
Knapsack 2 - greedy algorithms7 min
Knapsack 3 - modeling8 min
Knapsack 4 - dynamic programming17 min
Knapsack 5 - relaxation, branch and bound14 min
Knapsack 6 - search strategies, depth first, best first, least discrepancy14 min
Assignments Getting Started13 min
Knapsack & External Solver10 min
Exploring the Material - open course design, optimization landscape, picking your adventure10 min
17 heures pour terminer
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.
17 heures pour terminer
13 vidéos (Total 248 min), 1 lecture, 2 quiz
13 vidéos
CP 2 - propagation, arithmetic constraints, send+more=money26 min
CP 3 - reification, element constraint, magic series, stable marriage16 min
CP 4 - global constraint intuition, table constraint, sudoku19 min
CP 5 - symmetry breaking, BIBD, scene allocation18 min
CP 6 - redundant constraints, magic series, market split11 min
CP 7 - car sequencing, dual modeling18 min
CP 8 - global constraints in detail, knapsack, alldifferent33 min
CP 9 - search, first-fail, euler knight, ESDD25 min
CP 10 - value/variable labeling, domain splitting, symmetry breaking in search28 min
Graph Coloring6 min
Optimization Tools5 min
Set Cover8 min
1 lecture
Optimization Tools10 min
13 heures pour terminer
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.
13 heures pour terminer
10 vidéos (Total 191 min)
10 vidéos
LS 2 - swap neighborhood, car sequencing, magic square15 min
LS 3 - optimization, warehouse location, traveling salesman, 2-opt, k-opt23 min
LS 4 - optimality vs feasibility, graph coloring22 min
LS 5 - complex neighborhoods, sports scheduling21 min
LS 6 - escaping local minima, connectivity15 min
LS 7 - formalization, heuristics, meta-heuristics introduction22 min
LS 8 - iterated location search, metropolis heuristic, simulated annealing, tabu search intuition18 min
LS 9 - tabu search formalized, aspiration, car sequencing, n-queens26 min
Traveling Salesman10 min
Avis
Meilleurs avis pour OPTIMISATION DISCRÈTE
par SK29 mai 2019
Exceptional coverage of optimization fundamentals. Learning of practical applied methods. Real university level course, no water down "data science". Absolutely love it! Thank you professor Pascal.
par JB21 juil. 2017
Great course, the teacher is very clear and often goes into sufficient depth for the student to understand concepts. Assignments are challenging and help understanding course content.
par M2 juin 2020
This is, without a doubt, one of the most interesting courses I have ever taken. You will be challenged to create your own ideas and you will get to know what NP hard means in practice.
par AM5 févr. 2017
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.
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