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)
