Integer Programming and Combinatorial Optimization

Course objectives

The course aims to provide the basic notions of Integer Programming and Combinatorial Optimization. The expected learning outcomes consist of the ability to: 1. recognize Integer and Combinatorial Optimization problems in the application fields, also knowing how to evaluate their complexity and characteristics; 2. develop the mathematical modeling of the identified problems, writing optimization models for them; 3. practically solve the considered optimization models, by appropriately choosing the algorithms and solution software; 4. know how to interpret the solutions found in terms of the original application problems. In particular, referring to the Dublin Descriptors: Knowledge and understanding: On completion of the course, the students should have acquired knowledge of Integer Programming and Combinatorial Optimization, in particular with regards to the different types of mathematical models, solution algorithms and software solvers. Skills and abilities: On completion of the course, the students should be able to: - identify Integer and Combinatorial Optimization problems; - write an optimization model of an identified problem; - select an algorithm and a solution software to solve in practice the considered model. Judgement and communication skills: On completion of the course, the students should be able to: - judge the computational complexity of an Integer or Combinatorial Optimization problem and explain this to other colleagues without specific training on the subject; - judge whether a solution approach can practically obtain the solution; - understand the practical meaning of an obtained solution and explain it to other colleagues without specific training on the subject. Learning abilities: On completion of the course, the students should be able to learn and understand new types of models, algorithms and solution software to extend his/her skills in Optimization.

Channel 1
ANTONIO MARIA SUDOSO Lecturers' profile

Program - Frequency - Exams

Course program
Preliminaries of discrete mathematics and algorithms Properties of summations and geometric progressions Induction principle Definition of convex sets and convex functions. Local and global optimums, level line. Quadratic functions and convexity (positive semidefinite matrices) Linear functions, half-spaces and polyhedra. \item Solution of linear systems in two or three unknowns Convex programming, objective function and feasible region. Linear programming Standard and canonical form and graphical solution method. Example of execution of the simplex algorithm. Dictionaries and the revised simplex algorithm. Example of execution of the revised simplex algorithm. Duality theory, strong and weak duality and complementary gaps. Examples of linear programming models. Integer linear programming Notation, properties and linear relaxation Surrogate and Lagrangian relaxations Quality of formulations Logical implications and binary variables \item Examples of integer linear programming models Cutting planes based on Gomory cuts Branch-and-bound algorithm and its properties Branch-and-cut algorithm and its properties Linearization techniques for binary quadratic problems. Piecewise linear functions Computational complexity Sorting and running times of algorithms Knapsack problem Branch-and-bound algorithm for the knapsack problem Dynamic Programming Travelling Salesman Problem
Prerequisites
Operations Research
Books
- "Introduction to Mathematical Optimization" by Matteo Fischetti - "Linear Programming" by Vasek Chvatal - "Integer Programming" by Michele Conforti, Gérard Cornuéjols, Giacomo Zambelli - "Introduction to Linear Optimization" by Dimitris Bertsimas and John Tsitsiklis.
Exam mode
Written exam
Lesson mode
Frontal teaching
DAVIDE DONATO RUSSO Lecturers' profile
  • Lesson code10600389
  • Academic year2024/2025
  • CourseManagement Engineering
  • CurriculumBusiness intelligence and analytics (percorso formativo valido anche ai fini del conseguimento del doppio titolo italo-francese) - in inglese
  • Year1st year
  • Semester2nd semester
  • SSDMAT/09
  • CFU12
  • Subject areaAttività formative affini o integrative