813: Solving Business Problems Optimally with Data, with Jerry Yurchisin
Show notes
Jerry Yurchisin from Gurobi joins Jon Krohn 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: • The Burrito Optimization Game and mathematical optimization use cases [03:36] • Key differences between machine learning and mathematical optimization [05:45] • How mathematical optimization is ideal for real-world constraints [13:50] • Gurobi’s APIs and the ease of integrating them [21:33] • How LLMs like GPT-4 can help with optimization problems [39:39] • Why integer variables are so complex to model [01:02:37] • NP-hard problems [01:11:01] • The history of optimization and its early applications [01:26:23] Additional materials: www.superdatascience.com/813
Guests
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