Efficient methods in optimization


3 ECTS, C. 18h


Roland Hildebrand


This course deals with

  • Topic 1: convex analysis

  • Topic 2: convex programming

  • Basic notions: vector space, affine space, metric, topology, symmetry groups, linear and affine hulls, interior and closure, boundary, relative interior

  • Convex sets: definition, invariance properties, polyhedral sets and polytopes, simplices, convex hull, inner and outer description, algebraic properties, separation, supporting hyperplanes, extreme and exposed points, recession cone, Carathéodory number, convex cones, conic hull

  • Convex functions: level sets, support functions, sub-gradients, quasi-convex functions, self-concordant functions

  • Duality: dual vector space, conic duality, polar set, Legendre transform

  • Optimization problems: classification, convex programs, constraints, objective, feasibility, optimality, boundedness, duality

  • Linear programming: Farkas lemma, alternative, duality, simplex method

  • Algorithms: 1-dimensional minimization, Ellipsoid method, gradient descent methods, 2nd order methods

  • Conic programming: barriers, Hessian metric, duality, interior-point methods, universal barriers, homogeneous cones, symmetric cones, semi-definite programming

  • Relaxations: rank 1 relaxations for quadratically constrained quadratic programs, Nesterovs π/2 theorem, S-lemma, Dines theorem Polynomial optimization: matrix-valued polynomials in one variable, Toeplitz and Hankel matrices, moments, SOS relaxations


A two-hours written exam (E1) in December. For those who do not pass there will be another two-hours exam (E2) in session 2 in spring.