Nonlinear optimization and local models

Understand practical algorithms through local approximation, constraint qualifications, descent guarantees, and failure modes.

10 published lessons1 module327 min of guided study
  1. 01
    Nonlinear optimization and local modelsadvanced · 10 lessons

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Nonlinear optimization and local models

Understand practical algorithms through local approximation, constraint qualifications, descent guarantees, and failure modes.

  1. 01

    First- and second-order optimality

    Turn derivatives into local certificates, and recognize the cases where a Hessian test leaves the question open.

    29 min
  2. 02

    Descent directions and Wolfe line search

    Use decrease and curvature tests together to reject steps that are either wasteful or dangerously short.

    31 min
  3. 03

    Trust-region subproblems and model agreement

    Constrain a local quadratic model, compare prediction with reality, and handle negative curvature without an unchecked Newton step.

    32 min
  4. 04

    BFGS updates and curvature conditions

    Build a symmetric curvature model from gradient changes and see exactly why the positive-curvature test matters.

    32 min
  5. 05

    Nonlinear constraints and constraint qualifications

    Identify when linearized constraints faithfully support multiplier conditions and diagnose singular representations.

    34 min
  6. 06

    Sequential quadratic programming

    Derive a constrained Newton model and see why satisfying a tangent constraint does not guarantee nonlinear feasibility.

    34 min
  7. 07

    Augmented Lagrangian methods

    Combine multiplier correction with a finite penalty and distinguish inner stationarity from outer feasibility.

    33 min
  8. 08

    Primal-dual interior methods for nonlinear programs

    Follow perturbed complementarity with positive slacks and multipliers while keeping nonconvex guarantees distinct from convex ones.

    35 min
  9. 09

    Derivative-free models and direct search

    Extract local information from function values while accounting for geometry, sample cost, and evaluation noise.

    33 min
  10. 10

    Nonconvex complexity and stationarity certificates

    Derive a first-order iteration bound and state exactly what a small-gradient or approximate-curvature certificate proves.

    34 min
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