Instructors: Dr. Dominik Krupke and Gabriel Gehrke, TU Braunschweig, IBR, Algorithms Group
Optimization challenges are pervasive across numerous real-world applications within computer science, ranging from route planning to job scheduling. Certain problems, like the shortest path, can be solved efficiently and optimally with a solid theoretical foundation. However, a significant number of these challenges are classified as NP-hard, indicating that, for these problems, there is no known algorithm capable of consistently solving every instance efficiently to proven optimality. In such instances, heuristic approaches, such as genetic algorithms, are frequently employed as practical solutions. Yet, the question arises: Is it possible to devise algorithms that yield optimal solutions within a feasible timeframe for reasonably sized instances? This laboratory course is dedicated to exploring three sophisticated techniques that hold the potential for computing optimal solutions for a vast array of problems within practical limits. These techniques include:
- Constraint Programming with CP-SAT: This versatile methodology enables the definition of a problem’s constraints, upon which it employs a comprehensive suite of strategies, including the two techniques discussed below, to find optimal solutions.
- SAT Solvers: Renowned for their ability to resolve extensive logical formulas, these tools can be ingeniously adapted to address optimization challenges by transforming them into logical propositions.
- Mixed Integer Programming (MIP): This approach is adept at solving optimization problems characterized by integer and continuous variables under linear constraints.
For algorithm engineers and operations researchers, mastering these techniques opens the door to modeling and solving a wide spectrum of combinatorial optimization problems. By the end of this course, you will have acquired the skills to leverage these powerful methodologies, enabling you to approach NP-hard problems not only with theoretical insight but with practical, actionable solutions. This journey is not just about crafting elegant models but also about utilizing robust solution engines to navigate the complexities of NP-hard challenges effectively.
The class is organized into two main components: a series of exercises and an in-depth final project, both of which are designed to enhance your proficiency with key optimization techniques.
The exercise sheets will be conducted in pairs, while final projects will require collaboration among groups of three to four students. To ensure equitable team composition for the final projects, we may consider individual performance in the exercises as a criterion for team formation. Our objective is to create balanced teams by pairing students of comparable skill levels. This approach is informed by our observation that teams with a mix of varying abilities can sometimes lead to an imbalance, where more proficient students may inadvertently overshadow their peers.
To ensure you are thoroughly prepared for the project phase, this class will begin with a series of exercise sheets. These exercises are carefully crafted to either introduce you to new techniques or enhance your existing knowledge of them. Designed with a hands-on approach, these tasks aim to provide you with practical exposure to relevant tools and methodologies.
Each exercise sheet is allocated a two-week completion window. However, with new sheets released on a weekly basis, you effectively have one week to work on each exercise, with an additional week serving as a buffer. While the exercises are designed to be completed within a few hours, the learning curve associated with mastering new techniques may necessitate additional time. The time required to complete each sheet may vary; for example, you might spend more time on the initial sheet and less on subsequent ones, or vice versa, depending on your familiarity with the topics covered.
| Exercise Sheet 1 | Exercise Sheet 2 | Exercise Sheet 3 | Exercise Sheet 4 |
|---|---|---|---|
| Constraint Programming with CP-SAT | DIY: Branch and Bound | SAT Solver | Mixed Integer Programming |
| 2024-04-02 to 2023-04-16 | 2024-04-9 to 2024-04-23 | 2024-04-16 to 2023-04-30 | 2024-04-23 to 2024-05-07 |
| Here you will explore the use of CP-SAT, a declarative constraint programming solver. You will learn to define your problem mathematically, allowing CP-SAT to efficiently find solutions. | This exercise delves into the foundational algorithm behind generic solvers like CP-SAT. Participants will gain insights into what these solvers require for optimal performance by exploring the Branch and Bound algorithm. | After the high-level interface provided by CP-SAT, this exercise demands a closer interaction with the core mechanics of a SAT solver. You will learn to translate complex problems into basic logical formulas. | Learn about Mixed Integer Programming (MIP), a technique favored by many optimization experts. Although not as expressive as CP-SAT, MIP offers better scalability and the opportunity to apply various optimization tricks thanks to an extensive mathematical foundation. |
In the latter part of the course, we will embark on a comprehensive final project, marking an opportunity for you to apply the methodologies and strategies discussed in the exercises to a concrete, real-world challenge. This project phase, extending over several weeks, invites you to engage deeply with a problem, under the mentorship of your tutor. The culmination of this endeavor will be a presentation, wherein you will have the chance to exhibit the outcomes of your efforts.
The essence of the project phase is to immerse you in the practical application of optimization techniques, tackling a problem that demands a blend of innovative thinking and strategic planning. You are encouraged to explore diverse approaches to the problem, aiming to devise a persuasive and effective solution.
This semester, the project centers on creating a tool for SEP-assignments, addressing the longstanding issue of their complexity and inaccessibility. The challenge involves managing a database of projects and student preferences, with the goal of optimally assigning students to projects. This requires consideration of student preferences, project capacity constraints, and additional factors such as the distribution of skills (e.g., balancing the number of frontend and backend developers) and accommodating the preferences of institutes, particularly for students with whom they have previously worked. Your task is to select the most appropriate techniques learned during the course, apply them effectively, and convincingly present your solution in a competitive setting. While this project simulates a real-world consulting scenario, there exists the potential for your solution to be implemented in practice, should it prove sufficiently robust and innovative.
Please note, as a five-credit course, you are expected to dedicate approximately 150 hours in total to coursework. This allocation includes both the exercises and the project, with no more than 50 hours slated for the exercises. Consequently, a minimum of 100 hours should be devoted to the project, ensuring a deep and productive engagement with the material and the challenge at hand.
| Project (<- click for more details) |
|---|
| 2024-05-07 to 2024-07-12 |
| Use your newly acquired skills to tackle a real-world optimization challenge! |
For a successful experience in this course and to effectively work on the projects, students are expected to meet the following prerequisites:
- Proficiency in Python: The course's programming components will be exclusively conducted in Python. It is essential that you have a solid grasp of Python, as there will not be sufficient time to learn the language during the course.
- Algorithmic Foundations:
- Completion of Algorithms and Data Structures 1 is compulsory for foundational knowledge.
- It is advisable to have also completed (or complete in parallel) Algorithms and Data Structures 2 and Network Algorithms to be better prepared for the more complex topics.
- Additionally, it is beneficial to have attended Logic for Computer Scientist and Theoretical Computer Science I+II to be familiar with NP-hardness and propositional logic.
- Unix-Based Operating System:
- Access to a Unix system, which could be in the form of a virtual machine, is required for the course. Students should possess a fundamental understanding of Unix command-line operations.
- While most of the tools and software used in this course are compatible with Windows, support for Windows-specific issues cannot be guaranteed.
- Version Control with Git:
- A basic familiarity with Git is needed for version control purposes. While Git skills can be acquired swiftly, students are expected to learn them independently prior to or during the initial phase of the course.
Please ensure you meet these requirements to engage fully in the course activities. If you have any questions or need clarification on the prerequisites, feel free to reach out to us.
This class is just a quick peek into solving NP-hard problems in practice, there is more!
- Algorithm Engineering (Master, infrequently) will teach you a superset of this class, with more details.
- Mathematische Methoden der Algorithmik (Master) will teach you the theoretical background of Linear Programming and Mixed Integer Programming.
- Approximation Algorithms (Master) will teach you theoretical aspects of how to approximate NP-hard problems with guarantees. While this takes a theoretical point of view, the theoretical understanding can improve your practical skills on understanding and solving such problems.
- Discrete Optimization Course: For those who are want to dive really deep into the topic, I recommend doing this free course on Coursera in parallel. It is very intense but also very rewarding. Probably one of the best courses I have ever seen.
- In Pursuit of the Traveling Salesman: This amazing book is not a surprisingly good read, but also a great introduction to the field of optimization. It gives you a lot of the ideas that allow us to solve NP-hard problems in practice, while also gently introducing you to the way of thinking of an optimization expert.
Please let us know by opening an issue! You can also create a pull request on a separate branch, but as we have to do the changes in our internal repository (which also contains solutions), from which the public repository is automatically updated, it is easier for us if you open an issue and let us do the changes.
If you are an instructor and want to use this material in your course, feel free to do so! We are happy to share our material with you. If you have any questions, feel free to reach out to us. To get the solutions, please contact us directly from your official university email address, so we can verify that you are an instructor and not a student.