bnb-pep-skill

Formalize, derive, and locally implement Branch-and-Bound Performance Estimation Programming (BnB-PEP) instances from math or plain English. Use when a researcher or agent needs worst-case performance analysis or stepsize/parameter optimization of fixed-step first-order methods via performance estimation problems (PEP), translating a function class, finite interpolation conditions, method update equations, performance measure, and initial condition into the Generalized BnB-PEP derivation and Julia/JuMP models for the primal SDP, dual SDP, and Stage 1/2 local nonlinear workflows. Covers smooth (strongly) convex, smooth nonconvex, weakly convex, Lipschitz, indicator/support-function, and composite/proximal OptISTA-style setups. Also covers explicitly requested BnB-PEP Stage 3 spatial branch-and-bound planning and static linting, while default generation remains Stage 1/2 only.