Teaching code

4S00001

Teacher

Romeo Rizzi

Coordinator

Romeo Rizzi

Credits

6

Language

Italian

Scientific Disciplinary Sector (SSD)

MAT/09 - OPERATIONS RESEARCH

Period

Semester 2 dal Mar 3, 2025 al Jun 13, 2025.

Courses Single

Authorized

Lessons timetable Moodle Seminars 0

Learning objectives

The student will encounter in concrete the concepts of: problems, models, formulations of operations research, but also of instances, algorithms, reductions and mappings among problems of the computer science field. The course will propose some models of operations research, at least the following: linear programming (LP), integer linear programming (ILP), max-flows and min-cuts, bipartite matchings and node covers, minimum spanning trees, shortest paths, Eulerian paths, and some models resorting on dynamic programming among which some knapsack variants. For all these models/problems, except PLI, the student will learn the solving algorithms, the properties on which they hinge, and how to conduct their execution. However, besides and beyond this, the course aims at building a good and active relationship, practice, and acquaintance, with general mathematical methodologies and techniques (more typical of discrete math and for this reason not yet fully assimilated from our students) and some basic underpinnings of computer science. In particular, we insist on the dialog with problems and with the art/technique of conjecturing, no occasion is lost to spotlight where invariants and monovariants play a role in proofs, algorithms and data structures. We build up confidence with mathematical induction as an active tool for problem solving, and introducing the dialects of induction most voted to efficiency (divide et impera, recursion with memoization, dynamic programming). Some basic principles of informatics are underlined, like coding, algorithms, data structures, recursion as a counterpart of mathematical induction and of computability. (In some editions of the course first scratch introductions to numerability and computability have been offered). Coming to efficiency, our central perspective, the use of asymptotic notation is justified and adopted, the classes P, NP, coNP are introduced, and the concepts of good characterizations, good conjectures and good theorems are illustrated in length and complexity theory is advertised as a lively source of new methodologies in the art of facing problems and enquiry their intrinsic structural properties. Several aspects of the role and importance of the art of reducing one problem to another are discussed and clarified. The life cycle of a good conjecture, the workflow linking good conjectures and algorithms, the production and interpretation of counterexamples as a means of dialog with the problem, and the possible use of them in obtaining NP-completeness proofs, are all discussed, investigated and exemplified in action. Explicit emphasis is constantly given to the role and use of certificates. Meanwhile these transversal and high competences of methodological interest and imprinting are delivered, the students is asked to learn and develop several concrete procedural competences, in particular within LP, and in an algorithmic treatment of graph theory, introduced as a versatile model and an intuitive and expressive language for the formulation of problems. For a complete and detailed list of all these procedural competences delivered and requested, see the past exams and corrections over the various editions of the course. The notions from computational complexity introduced in the course, and the attention to the languages of the certificates, will lead the student to recognize with more awareness the structure of a sound proof. Dealing with instances, problems, models, both from the perspective of algorithms and of models and formulations, will enforce the attitude and competence in casting simple problems from the applications into mathematical models. The knowledge of the paradigmatic results of linear programming theory (duality, complementary slackness, economic interpretation, sensitivity analysis) will provide the student with important tools in obtaining non-trivial insights on the practical problem from the model.

Prerequisites and basic notions

rudiments of analysis (numbers, sets, functions), algebra and calculus (equations and unknowns), analytic geometry (Cartesian coordinates,
equations for the line or the plane), and linear algebra (vectors and matrices)

Program

Operations Research offers quantitative methods and models for the optimal management of resources, and optimization of profits, services, strategies, procedures.
This course of Operations Research gets to Mathematical Programming moving from Algorithmics and Computational Complexity.
After revisiting mathematical induction, recursion, divide et impera, with a curiosity driven problem solving approach, we insist on dynamic programming thinking which gets then exemplified in a few classical models of Operations Research and Computational Biology.
With emphasis on method and techniques, we get involved in formulating, encoding and modeling problems, conjecturing about them, reducing one to the other,
and well characterizing them.
The course offers an in-depth introduction to linear programming.
Following the historical path, we introduce graphs as for modeling,
and explore the basic fundamental results in combinatorial optimization and graph theory.
LIST OF TOPICS:
1. Basic Notions
problems
models
algorithms
complexity
2. Introduction to Algorithms and Complexity
analysis of a few algorithms
design techniques (recursion, divide et impera, recursion with memoization, dynamic programming, greedy)
complexity theory (P, NP, co-NP, good characterizations, good conjectures, examples of NP-completeness proofs)
3. Combinatorial Optimization Models
knapsack problems
Problems on sequences
Problems on DAGs
4. Introduction to Graph Theory
graphs and digraphs as models
a few good characterizations (bipartite, Eulerian, acyclic, planar graphs)
a few NP-hard models (Hamiltonian cycles, cliques, colorability)
shortest paths
minimum spanning trees
maximum flows
bipartite matchings
5. Linear Programming (LP)
the LP and the ILP models (definition, motivations, complexity, role)
geometric method and view (feasibility space,
pivot, duality, dual variables, degeneracy, complementary slackness)
standard and canonical form
simplex method
duality theory
complementary slackness
economic interpretation of the dual variables
sensitivity analysis
BOOKS, NOTES AND OTHER DIDACTIC MATERIALS AND RESOURCES:
At the following page you find a list of available materials (books, notes, videos) about topics covered within the course:
http://profs.sci.univr.it/~rrizzi/classes/RO/materiali
From Levanto you can access to the .pdf of some reference book.
If you find out further effective material help us enlarging this list.

Bibliography

Didactic methods

In presence. The lessons will be recorded as possible.
Our main drive and focus is acquiring active skills and competences rather than a wealth of dry theoretical knowledge.
The active and collaborative participation of the students is welcome and precious.
The Telegram Group https://t.me/RicercaOperativaUniVR is a first reference for the course and keeps us all-2-all connected.
The list of bibliographic materials freely available through the university's Levanto service is https://univr.alma.exlibrisgroup.com/leganto/public/39UVR_INST/lists/5423473240005791?auth=SAML

Learning assessment procedures

At the end of the course, a written exam with various types of exercises and questions on the more procedural competences acquired during the course. You can add (in full or in part) to the mark acquired at the exam by conducting projects aiming at improving aspects and/or materials of the course in a broad sense.
The exam is the very same regardless on whether you have attended or not the course. The archives of the past exams, the relative corrections, and the videos of the classes, all can help overcoming the difficulties of the non-attending student. Despite these resources, the more methodological messages of the course remain difficult to acquire without active participation and attendance to the lessons, and this can penalize the student, also at the exam.

Evaluation criteria

With your help, we seek and build transparency on the correction procedures and evaluation mechanisms. From the very start, we underline a peculiarity of the Operations Research course, the only one in discrete mathematics at the bachelor:
in evaluating the answers we attach great importance to the quality of the certificates produced.
In fact, the approach and spirit with which you should approach the course and the exam, and what to deliver and elaborate in your answers to the exercises, is actually related to some deep methodological messages that we decided to place at the core of the course. The more the student adopts and interprets these approaches, the more enriching the course and the easier the exam. This will be important to get the most from the course and perform well during the exam.

Criteria for the composition of the final grade

We do not give any weight to wrong answers (really, we are not scandalized by anything). Only correct answers count for us.
We add up the points collected through these, and this sum is mapped into the vote via a function that we guarantee to be monotonic continuous. We reserve the right to redesign this function after the conclusion of the task (to make the necessary adjustments) but typically it is the sum of the points shown in the test text.
The mark produced remains valid until another submission at the end of a subsequent exam session.

Exam language

italiano