Piecewise Linear Magic: Cracking Nonlinear Optimization Nightmares in Python
Nonlinear optimization problems lurk in every quant's code, from portfolios to pipelines. Piecewise linear approximations turn them into fast LPs—here's the data-driven proof and Python playbook.
⚡ Key Takeaways
- Piecewise linear approximations convert tough NLPs to fast LPs using SOS2, slashing solve times dramatically.
- Ideal for separable functions in finance portfolios and AI tuning; introduce aux vars for cross terms.
- Python + Gurobi makes it dead simple—exact on convex with enough segments.
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Originally reported by Towards Data Science
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