CTU FEE Moodle
Combinatorial Optimization
B232 - Summer 23/24
This is a grouped Moodle course. It consists of several separate courses that share learning materials, assignments, tests etc. Below you can see information about the individual courses that make up this Moodle course.
Combinatorial Optimization - A4M35KO
Main course
| Credits | 6 |
| Semesters | Summer |
| Completion | Assessment + Examination |
| Language of teaching | Czech |
| Extent of teaching | 3P+2C |
Annotation
The goal is to show the problems and algorithms of combinatorial optimization (often called discrete optimization; there is a strong overlap with the term operations research).
Following the courses on linear algebra, graph theory, and basics of optimization, we show optimization techniques based on graphs, integer linear programming, heuristics, approximation algorithms and state space search methods.
We focus on application of optimization in stores, ground transportation, flight transportation, logistics, planning of human resources, scheduling in production lines, message routing, scheduling in parallel computers.
Following the courses on linear algebra, graph theory, and basics of optimization, we show optimization techniques based on graphs, integer linear programming, heuristics, approximation algorithms and state space search methods.
We focus on application of optimization in stores, ground transportation, flight transportation, logistics, planning of human resources, scheduling in production lines, message routing, scheduling in parallel computers.
Study targets
No data.
Course outlines
1. Introduction to Basic Terms of Combinatorial Optimization, Example Applications and a Test of Preliminary Knowledge
2. Integer Linear Programming - Algorithms
3. Problem Formulation by Integer Linear Programming
4. The Shortest Paths. Problem Formulation by Shortest Paths.
5. Problem Formulation by Shortest Paths.
6. Flows and Cuts - Algorithms and Problem Formulation. Test I.
7. Multicommodity network flows
8. Knapsack Problem and Pseudo-polynomial Algorithms
9. Traveling Salesman Problem and Approximation Algorithms
10. Monoprocessor Scheduling
11. Scheduling on Parallel Processors. Test II.
12. Project Scheduling with Time Windows.
13. Constraint Programming.
14. Reserved
2. Integer Linear Programming - Algorithms
3. Problem Formulation by Integer Linear Programming
4. The Shortest Paths. Problem Formulation by Shortest Paths.
5. Problem Formulation by Shortest Paths.
6. Flows and Cuts - Algorithms and Problem Formulation. Test I.
7. Multicommodity network flows
8. Knapsack Problem and Pseudo-polynomial Algorithms
9. Traveling Salesman Problem and Approximation Algorithms
10. Monoprocessor Scheduling
11. Scheduling on Parallel Processors. Test II.
12. Project Scheduling with Time Windows.
13. Constraint Programming.
14. Reserved
Exercises outlines
1. Policy and Individual Project Market
2. Introduction to the Experimental Environment and Optimization Library
3. Integer Linear Programming
4. Individual Project I - Assignment and Problem Classification
5. Modeling Languages for Solving Combinatorial Problems
6. Individual Project II - Related Work and Solution
7. Applications of Network Flows and Cuts
8. Individual Project III - Consultation
9. Test III
10. Scheduling
11. Advanced Methods for Solving Combinatorial Problems
12. Individual Project IV - hand in a code and a written report
13. Ungraded Assessment
14. Reserved
2. Introduction to the Experimental Environment and Optimization Library
3. Integer Linear Programming
4. Individual Project I - Assignment and Problem Classification
5. Modeling Languages for Solving Combinatorial Problems
6. Individual Project II - Related Work and Solution
7. Applications of Network Flows and Cuts
8. Individual Project III - Consultation
9. Test III
10. Scheduling
11. Advanced Methods for Solving Combinatorial Problems
12. Individual Project IV - hand in a code and a written report
13. Ungraded Assessment
14. Reserved
Literature
B. H. Korte and J. Vygen, Combinatorial Optimization: Theory and Algorithms.
Springer, third ed., 2006.
J. Blazevicz, Scheduling Computer and Manufacturing Processes. Springer,
second ed., 2001.
J. Demel, Grafy a jejich aplikace. Academia, second ed., 2002.
TORSCHE http://rtime.felk.cvut.cz/scheduling-toolbox/
Springer, third ed., 2006.
J. Blazevicz, Scheduling Computer and Manufacturing Processes. Springer,
second ed., 2001.
J. Demel, Grafy a jejich aplikace. Academia, second ed., 2002.
TORSCHE http://rtime.felk.cvut.cz/scheduling-toolbox/
Requirements
Optimisation, Discrete mathematics, Logics and graphs
Combinatorial Optimization - AD4M35KO
| Credits | 6 |
| Semesters | Summer |
| Completion | Assessment + Examination |
| Language of teaching | Czech |
| Extent of teaching | 21KP+6KC |
Annotation
The goal is to show the problems and algorithms of combinatorial optimization (often called discrete optimization; there is a strong overlap with the term operations research).
Following the courses on linear algebra, graph theory, and basics of optimization, we show optimization techniques based on graphs, integer linear programming, heuristics, approximation algorithms and state space search methods.
We focus on application of optimization in stores, ground transportation, flight transportation, logistics, planning of human resources, scheduling in production lines, message routing, scheduling in parallel computers.
Following the courses on linear algebra, graph theory, and basics of optimization, we show optimization techniques based on graphs, integer linear programming, heuristics, approximation algorithms and state space search methods.
We focus on application of optimization in stores, ground transportation, flight transportation, logistics, planning of human resources, scheduling in production lines, message routing, scheduling in parallel computers.
Study targets
No data.
Course outlines
1. Introduction to Basic Terms of Combinatorial Optimization, Example Applications and a Test of Preliminary Knowledge
2. Integer Linear Programming - Algorithms
3. Problem Formulation by Integer Linear Programming
4. The Shortest Paths. Problem Formulation by Shortest Paths.
5. Problem Formulation by Shortest Paths.
6. Flows and Cuts - Algorithms and Problem Formulation. Test I.
7. Multicommodity network flows
8. Knapsack Problem and Pseudo-polynomial Algorithms
9. Traveling Salesman Problem and Approximation Algorithms
10. Monoprocessor Scheduling
11. Scheduling on Parallel Processors. Test II.
12. Project Scheduling with Time Windows.
13. Constraint Programming.
14. Reserved
2. Integer Linear Programming - Algorithms
3. Problem Formulation by Integer Linear Programming
4. The Shortest Paths. Problem Formulation by Shortest Paths.
5. Problem Formulation by Shortest Paths.
6. Flows and Cuts - Algorithms and Problem Formulation. Test I.
7. Multicommodity network flows
8. Knapsack Problem and Pseudo-polynomial Algorithms
9. Traveling Salesman Problem and Approximation Algorithms
10. Monoprocessor Scheduling
11. Scheduling on Parallel Processors. Test II.
12. Project Scheduling with Time Windows.
13. Constraint Programming.
14. Reserved
Exercises outlines
1. Policy and Individual Project Market
2. Introduction to the Experimental Environment and Optimization Library
3. Integer Linear Programming
4. Individual Project I - Assignment and Problem Classification
5. Modeling Languages for Solving Combinatorial Problems
6. Individual Project II - Related Work and Solution
7. Applications of Network Flows and Cuts
8. Individual Project III - Consultation
9. Test III
10. Scheduling
11. Advanced Methods for Solving Combinatorial Problems
12. Individual Project IV - hand in a code and a written report
13. Ungraded Assessment
14. Reserved
2. Introduction to the Experimental Environment and Optimization Library
3. Integer Linear Programming
4. Individual Project I - Assignment and Problem Classification
5. Modeling Languages for Solving Combinatorial Problems
6. Individual Project II - Related Work and Solution
7. Applications of Network Flows and Cuts
8. Individual Project III - Consultation
9. Test III
10. Scheduling
11. Advanced Methods for Solving Combinatorial Problems
12. Individual Project IV - hand in a code and a written report
13. Ungraded Assessment
14. Reserved
Literature
B. H. Korte and J. Vygen, Combinatorial Optimization: Theory and Algorithms.
Springer, third ed., 2006.
J. Blazevicz, Scheduling Computer and Manufacturing Processes. Springer,
second ed., 2001.
J. Demel, Grafy a jejich aplikace. Academia, second ed., 2002.
TORSCHE http://rtime.felk.cvut.cz/scheduling-toolbox/
Springer, third ed., 2006.
J. Blazevicz, Scheduling Computer and Manufacturing Processes. Springer,
second ed., 2001.
J. Demel, Grafy a jejich aplikace. Academia, second ed., 2002.
TORSCHE http://rtime.felk.cvut.cz/scheduling-toolbox/
Requirements
Optimisation, Discrete mathematics, Logics and graphs
Combinatorial Optimization - AE4M35KO
| Credits | 6 |
| Semesters | Summer |
| Completion | Assessment + Examination |
| Language of teaching | English |
| Extent of teaching | 3P+2C |
Annotation
The goal is to show the problems and algorithms of combinatorial optimization (often called discrete optimization; there is a strong overlap with the term operations research).
Following the courses on linear algebra, graph theory, and basics of optimization, we show optimization techniques based on graphs, integer linear programming, heuristics, approximation algorithms and state space search methods.
We focus on application of optimization in stores, ground transportation, flight transportation, logistics, planning of human resources, scheduling in production lines, message routing, scheduling in parallel computers.
Following the courses on linear algebra, graph theory, and basics of optimization, we show optimization techniques based on graphs, integer linear programming, heuristics, approximation algorithms and state space search methods.
We focus on application of optimization in stores, ground transportation, flight transportation, logistics, planning of human resources, scheduling in production lines, message routing, scheduling in parallel computers.
Study targets
No data.
Course outlines
1. Introduction to Basic Terms of Combinatorial Optimization, Example Applications and a Test of Preliminary Knowledge
2. Integer Linear Programming - Algorithms
3. Problem Formulation by Integer Linear Programming
4. The Shortest Paths. Problem Formulation by Shortest Paths.
5. Problem Formulation by Shortest Paths.
6. Flows and Cuts - Algorithms and Problem Formulation. Test I.
7. Multicommodity network flows
8. Knapsack Problem and Pseudo-polynomial Algorithms
9. Traveling Salesman Problem and Approximation Algorithms
10. Monoprocessor Scheduling
11. Scheduling on Parallel Processors. Test II.
12. Project Scheduling with Time Windows.
13. Constraint Programming.
14. Reserved
2. Integer Linear Programming - Algorithms
3. Problem Formulation by Integer Linear Programming
4. The Shortest Paths. Problem Formulation by Shortest Paths.
5. Problem Formulation by Shortest Paths.
6. Flows and Cuts - Algorithms and Problem Formulation. Test I.
7. Multicommodity network flows
8. Knapsack Problem and Pseudo-polynomial Algorithms
9. Traveling Salesman Problem and Approximation Algorithms
10. Monoprocessor Scheduling
11. Scheduling on Parallel Processors. Test II.
12. Project Scheduling with Time Windows.
13. Constraint Programming.
14. Reserved
Exercises outlines
1. Policy and Individual Project Market
2. Introduction to the Experimental Environment and Optimization Library
3. Integer Linear Programming
4. Individual Project I - Assignment and Problem Classification
5. Modeling Languages for Solving Combinatorial Problems
6. Individual Project II - Related Work and Solution
7. Applications of Network Flows and Cuts
8. Individual Project III - Consultation
9. Test III
10. Scheduling
11. Advanced Methods for Solving Combinatorial Problems
12. Individual Project IV - hand in a code and a written report
13. Ungraded Assessment
14. Reserved
2. Introduction to the Experimental Environment and Optimization Library
3. Integer Linear Programming
4. Individual Project I - Assignment and Problem Classification
5. Modeling Languages for Solving Combinatorial Problems
6. Individual Project II - Related Work and Solution
7. Applications of Network Flows and Cuts
8. Individual Project III - Consultation
9. Test III
10. Scheduling
11. Advanced Methods for Solving Combinatorial Problems
12. Individual Project IV - hand in a code and a written report
13. Ungraded Assessment
14. Reserved
Literature
B. H. Korte and J. Vygen, Combinatorial Optimization: Theory and Algorithms.
Springer, third ed., 2006.
J. Blazevicz, Scheduling Computer and Manufacturing Processes. Springer,
second ed., 2001.
J. Demel, Grafy a jejich aplikace. Academia, second ed., 2002.
TORSCHE http://rtime.felk.cvut.cz/scheduling-toolbox/
Springer, third ed., 2006.
J. Blazevicz, Scheduling Computer and Manufacturing Processes. Springer,
second ed., 2001.
J. Demel, Grafy a jejich aplikace. Academia, second ed., 2002.
TORSCHE http://rtime.felk.cvut.cz/scheduling-toolbox/
Requirements
Optimisation, Discrete mathematics, Logics and graphs