Syllabus
Variational Analysis
1) Convex analysis in finite dimension.
2) Cones and cosmic closure.
3) Set convergence.
4) Set-valued mappings.
5) Epi-convergence.
6) Variation analysis.
7) Subgradient and subdiferential.
8) Lipschitz properties.
9) Legendre-Fenchel duality. Sensitivity of stochastic programming
1) Stability in stochastic programming.
2) Methods of parametric optimization. Probabilistic metrics.
3) Methods of asymptotic and robust statistics.
Annotation
The lecture is oriented to base of modern optimization and stability in stochastic programming. The lecture is devoted to doctoral students.