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.

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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