Course Summary
Learn how to solve optimization problems in discrete mathematics with this course. Discover the different algorithms and techniques used in optimization and apply them to real-world scenarios.Key Learning Points
- Gain knowledge on discrete optimization problems and their applications.
- Learn different algorithms and techniques for optimization.
- Apply optimization techniques to real-world scenarios.
- Improve problem-solving and analytical skills.
- Collaborate with fellow learners and work on assignments and projects.
Job Positions & Salaries of people who have taken this course might have
- USA: $84,810
- India: ₹1,065,000
- Spain: €35,000
- USA: $84,810
- India: ₹1,065,000
- Spain: €35,000
- USA: $121,189
- India: ₹1,467,000
- Spain: €38,000
- USA: $84,810
- India: ₹1,065,000
- Spain: €35,000
- USA: $121,189
- India: ₹1,467,000
- Spain: €38,000
- USA: $112,416
- India: ₹1,569,000
- Spain: €36,000
Related Topics for further study
Learning Outcomes
- Understand the fundamentals of discrete optimization problems and their applications.
- Apply different optimization algorithms and techniques to solve real-world problems.
- Collaborate with other learners and develop problem-solving and analytical skills.
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of mathematics and computer science.
- Familiarity with algorithms and data structures.
Course Difficulty Level
IntermediateCourse Format
- Online self-paced course.
- Includes video lectures, quizzes, and programming assignments.
- Collaborative learning environment.
Similar Courses
- Linear and Integer Programming
- Discrete Mathematics
Related Education Paths
Notable People in This Field
- Laura Albert
- Robert Fourer
Related Books
Description
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.
Outline
- 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
Summary of User Reviews
The Discrete Optimization course on Coursera has received positive reviews from many users. Students appreciate the challenging yet rewarding coursework, engaging lectures, and helpful feedback from the instructor. One key aspect that many users thought was good is the hands-on approach to learning through programming assignments and real-world case studies.Pros from User Reviews
- Challenging and rewarding coursework
- Engaging lectures
- Helpful feedback from the instructor
- Hands-on approach to learning through programming assignments and real-world case studies
- Good preparation for future academic and professional pursuits
Cons from User Reviews
- Some students found the course material to be too advanced or difficult
- Limited interaction with other students
- Course content may not be applicable to all fields or industries
- Some technical issues with the online platform
- High workload and time commitment
Professor Pascal Van Hentenryck
4.8 Raiting
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