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Algorithms For Constrained Optimization


Veronica Piccialli
Università di Roma Tor Vergata
Course Type
Type A
May 24 -- May 28 h. 14:30-16.30
May 31 -- June 4 h. 14:30-16.30
Optimality conditions for unconstrained optimization and constrained optimization. Special cases: convex feasible set, linear constraints, box constraints. Krush-Kuhn-Tucker conditions Unconstrained Optimization Algorithms: exact line search, Armijo line search. Gradient method. Algorithms for Constrained Optimization Problems with convex feasible set:
-- Frank Wolfe method
-- Projected gradient method
Algorithms for Constrained Optimization with general constraints:
-- Sequential penalty method
-- Augmented Lagrangian
-- Exact penalty functions
-- Exact Augmented Lagrangian
Quadratic Programming:
-- Wolfe duality theory
-- An application: training of a Support Vector Machine (SVM)
-- Hints on decomposition methods for SVM



PhD Students/Alumni

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