Docente
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SAGRATELLA SIMONE
(programma)
Part 1: Mathematical programming
Things to know:
- structure of an optimization problem
- feasiblility, unboundedness, and optimality
- active, satisfied, violated, and redundant constraints
Abilities to acquire:
- recast a model as a problem in general form
- understand if an optimization problem is an LP, an ILP, or an NLP
Part 2: Linear Programming
Things to know:
- properties of linear functions
- convex sets and polyhedra
- vertices of a polyhedron and their properties
- fundamental theorem of LP
- solution set properties
Abilities to acquire:
- graphical solution
- computation of all the vertices of a polyhedron
- (graphical) simplex algorithm
Part 3: Integer Linear Programming
Things to know:
- linear formulations and their properties
- optimal linear formulations
Abilities to acquire:
- computation of an optimal linear formulation
- branch and bound method for knapsack problems and graphical solution
Part 4: NonLinear Programming
Things to know:
- local and global solutions
- feasiblility, unboundedness, and optimality in nonlinear programming
- convex problems and their properties
Abilities to acquire:
- understand if a problem is (strictly) convex
lecture notes of the teacher
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