Lecture
Optimal Decision Making: Applications of Discrete Optimization
Description
This lecture covers the concept of optimal decision making using discrete optimization, focusing on binary variables and their applications in practical problems. Topics include mixed-integer linear programming, the 0-1 knapsack problem, and modeling with binary variables. The instructor discusses LP relaxation, the strength of formulations, and modeling with exponentially many constraints.
This video is available exclusively on Mediaspace for a restricted audience. Please log in to MediaSpace to access it if you have the necessary permissions.
Watch on MediaspaceInstructor
nulla Lorem
Anim cupidatat mollit ad consectetur cupidatat sit sit adipisicing qui id ad non exercitation quis. Eiusmod id ea est excepteur voluptate minim culpa culpa laboris. Adipisicing velit fugiat tempor exercitation duis incididunt proident et cupidatat laboris Lorem consequat quis. Cillum irure voluptate nulla dolore amet Lorem et esse pariatur. Ad in minim tempor commodo laboris officia in tempor id laboris cupidatat id exercitation ullamco. Incididunt ad do et eiusmod proident enim non ad aliquip. Proident sunt tempor pariatur irure ipsum aliqua exercitation ex.
Official source
About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Ontological neighbourhood
Related lectures (43)
Exact methods: Branch and BoundMOOC: Optimization: principles and algorithms - Linear optimization
Explores the Branch and Bound algorithm in discrete optimization, efficiently finding optimal solutions by calculating lower bounds on subsets.
Optimization Methods: Theory Discussion
Explores optimization methods, including unconstrained problems, linear programming, and heuristic approaches.
Thermodynamic Properties: Equations and Models
Explains thermodynamic properties, equations of state, and mixture rules for energy systems modeling.
Mixed-Integer Linear Programming: Formulations and Applications
Explores mixed-integer linear programming, binary variables, 0-1 knapsack, assignment problems, and LP relaxation strength.
Optimisation in Energy Systems
Explores optimization in energy system modeling, covering decision variables, objective functions, and different strategies with their pros and cons.