1 / 8

Unlocking Discrete Optimization: The Relationship Between Counting Techniques and Graph Theory

Learn how adding an objective function transforms counting problems to discrete optimization challenges, with examples from job scheduling and network optimization. Discover the role of algorithms in solving optimization problems efficiently.

eshelton
Download Presentation

Unlocking Discrete Optimization: The Relationship Between Counting Techniques and Graph Theory

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Discrete Optimization

  2. The relationship between counting techniques/graph theory and discrete optimization Adding a goal (objective function) to a counting situation or a graph model makes it a discrete optimization problem.

  3. Job 3 Job 1 Job 4 Job 2 Example: making the scheduling model an optimization problem • There are 4 jobs that should be processed on the same machine. (Can’t be processed simultaneously). Here is an example of a possible schedule: • Original (counting) question: What is the number of all possible schedules? • New (optimization) question: Find a schedule that minimizes the average completion time of the four jobs.

  4. Example: making a graph model an optimization problem • There are n cities. The salesman  starts his tour from City 1,  visits each of the cities exactly once,  and returns to City 1. Original (counting) question : How many different tours are possible? New (optimization) question : Find a minimum-cost tour.

  5. Graphs and discrete optimization Adding a goal (objective function) about the amount of some entity in a network makes the network model a discrete (network) optimization problem. • Goal: Build a network satisfying certain requirements with minimal cost • Move some entity (electricity, a consumer product, people, information) from one point to another in underlying network as efficiently as possible: • Provide good service to the network users • Use the network facilities efficiently

  6. Ingredients of some common physical networks

  7. How to solve discrete optimization problems? Design algorithms! The word algorithm refers to a step-by-step method for performing some action. Some examples of algorithms in everyday life: • Food preparation recipes • Driving directions • Directions for assembling equipment • Instructions for filling out income tax forms We will study algorithms for solving discrete optimization problems

  8. Solution Process for discrete optimization problems Optimiz. models Abstraction, simplifications Design algorithms The role of Discrete Mathematics : • Show that the algorithm is correct • Show that the algorithm is efficient • Do careful mathematical analysis to design better algorithms Solution to the model Real life situation

More Related