University of Melbourne - Discrete Optimization
- Offered byCoursera
Discrete Optimization at Coursera Overview
Duration | 65 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Discrete Optimization at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Intermediate Level
- Approx. 65 hours to complete
- English Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish
Discrete Optimization at Coursera Course details
- 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.
Discrete Optimization at Coursera Curriculum
Welcome
Course Promo
Course Motivation - Indiana Jones, challenges, applications
Course Introduction - philosophy, design, grading rubric
Assignments Introduction & Any Integer
Start of Course Survey
Course Syllabus
Knapsack
Knapsack 1 - intuition
Knapsack 2 - greedy algorithms
Knapsack 3 - modeling
Knapsack 4 - dynamic programming
Knapsack 5 - relaxation, branch and bound
Knapsack 6 - search strategies, depth first, best first, least discrepancy
Assignments Getting Started
Knapsack & External Solver
Exploring the Material - open course design, optimization landscape, picking your adventure
Constraint Programming
CP 1 - intuition, computational paradigm, map coloring, n-queens
CP 2 - propagation, arithmetic constraints, send+more=money
CP 3 - reification, element constraint, magic series, stable marriage
CP 4 - global constraint intuition, table constraint, sudoku
CP 5 - symmetry breaking, BIBD, scene allocation
CP 6 - redundant constraints, magic series, market split
CP 7 - car sequencing, dual modeling
CP 8 - global constraints in detail, knapsack, alldifferent
CP 9 - search, first-fail, euler knight, ESDD
CP 10 - value/variable labeling, domain splitting, symmetry breaking in search
Graph Coloring
Optimization Tools
Set Cover
Optimization Tools
Local Search
LS 1 - intuition, n-queens
LS 2 - swap neighborhood, car sequencing, magic square
LS 3 - optimization, warehouse location, traveling salesman, 2-opt, k-opt
LS 4 - optimality vs feasibility, graph coloring
LS 5 - complex neighborhoods, sports scheduling
LS 6 - escaping local minima, connectivity
LS 7 - formalization, heuristics, meta-heuristics introduction
LS 8 - iterated location search, metropolis heuristic, simulated annealing, tabu search intuition
LS 9 - tabu search formalized, aspiration, car sequencing, n-queens
Traveling Salesman
Linear Programming
LP 1 - intuition, convexity, geometric view
LP 2 - algebraic view, naive algorithm
LP 3 - the simplex algorithm
LP 4 - matrix notation, the tableau
LP 5 - duality derivation
LP 6 - duality interpretation and uses
Mixed Integer Programming
MIP 1 - intuition, relaxation, branch and bound, knapsack, warehouse location
MIP 2 - modeling, big-M, warehouse location, graph coloring
MIP 3 - cutting planes, Gomory cuts
MIP 4 - convex hull, polyhedral cuts, warehouse location, node packing, graph coloring
MIP 5 - cover cuts, branch and cut, seven bridges, traveling salesman
Facility Location
Advanced Topics: Part I
Scheduling - jobshop, disjunctive global constraint
Vehicle Routing
Advanced Topics: Part II
Large Neighborhood Search - asymmetric TSP with time windows
Column Generation - branch and price, cutting stock
End of course survey
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