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Episode
813: Solving Business Problems Optimally with Data — with Jerry Yurchisin
Views
1K
0.1× the show’s average
Episode1K
Show avg17K
Published
2y ago
Aug 27, 2024
Length
1h 42m
show avg 1h 09m
From the show
Chapters
#MathematicalOptimization #OptimizationProblems #DataDrivenDecisionMaking Jerry Yurchisin from Gurobi joins @JonKrohnLearns to break down mathematical optimization, showing why it often outshines machine learning for real-world challenges. Find out how innovations like NVIDIA’s latest CPUs are speeding up solutions to problems like the Traveling Salesman in seconds. Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information. In this episode you will learn: Additional materials: https://www.superdatascience.com/813
- 0:00Introduction
- 2:06The Burrito Optimization Game and mathematical optimization use cases
- 4:15Key differences between machine learning and mathematical optimization
- 12:20How mathematical optimization is ideal for real-world constraints
- 20:03Gurobi’s APIs and the ease of integrating them
- 38:07How LLMs like GPT-4 can help with optimization problems
- 1:01:05Why integer variables are so complex to model
- 1:09:31NP-hard problems
- 1:24:51The history of optimization and its early applications
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