CODE 108960 ACADEMIC YEAR 2025/2026 CREDITS 6 cfu anno 2 MATEMATICA 9011 (LM-40) - GENOVA 6 cfu anno 1 MATEMATICA 11907 (LM-40 R) - GENOVA 6 cfu anno 3 MATEMATICA 8760 (L-35) - GENOVA SCIENTIFIC DISCIPLINARY SECTOR MAT/09 LANGUAGE Italian TEACHING LOCATION GENOVA SEMESTER 1° Semester OVERVIEW The classes aim to give students the tools and training to understand and use the main convex optimization and operational research algorithms. The classes present the basic theory, and it focuses on modeling aspects and results that are useful in applications to machine learning and inverse problems. AIMS AND CONTENT LEARNING OUTCOMES The aim of the course is to provide the tools for theoretical understanding and practical use of the main optimization algorithms used for data analysis. AIMS AND LEARNING OUTCOMES At the end of the classes the students will have a working knowledge of convex optimzation and linear programming. In particular, they will have the skills to: Recognize convex and linear programming problems, understand and use convex and linear programming optimization algorithms, solve convex and linear programming problems in real scenarios. PREREQUISITES Calculus for functions of several variables, linear algebra, probability TEACHING METHODS Classes on the blackboard in which theoretical concepts and algorithms will be introduced from a theoretical point of view and labs where notebooks will guide the student in the implementation and the use of convex optimization and linear programming algorithms to solve real problems. SYLLABUS/CONTENT The classes will cover the basic notions for optimization problems. They will cover the linear programming problem, and optimization algorithms for the minimization of smooth and nonsmooth convex functions. The course will discuss the convergence properties of gradient descent, stochastic gradient descent and proximal gradient descent. Applications to machine learning and imaging problems will be implemented and used during the lab sessions. The teaching will contribute to the following objectives and goals for the Agenda 2030 for sustainable development: Goal 4. Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all Goal 5. Achieve gender equality and empower all women and girls RECOMMENDED READING/BIBLIOGRAPHY S. Boyd and L. Vandenberghe, Convex Optimization, Cambridge University Press, 2004 S. Bubeck, Convex Optimization: Algorithms and Complexity, https://arxiv.org/abs/1405.4980?context=cs S. Salzo, S. Villa, Proximal Gradient Methods for Machine Learning and Imaging, 2022 TEACHERS AND EXAM BOARD SILVIA VILLA Ricevimento: By appointment wich can be fixed in person or via email : silvia.villa@unige.it LESSONS LESSONS START According to the academic calendar Class schedule The timetable for this course is available here: Portale EasyAcademy EXAMS EXAM DESCRIPTION There are two examination options. The first consists of written (and laboratory) mid-term tests, which involve the application of the concepts introduced during the course. The final grade is calculated as the average of the lab report evaluations and the mid-term tests (provided all three evaluations are satisfactory). Students may choose to complete the exam with an oral test or keep the grade obtained from the written tests and laboratory work. If any of the lab reports are unsatisfactory, they must be corrected and resubmitted. If one of the written tests is unsatisfactory, the student may choose either to take an oral test on the failed part or to take an oral exam covering the entire syllabus. The second option consists of a single oral exam at the end of the course covering the entire syllabus, in addition to the submission of all lab reports. Students with certified learning disabilities (DSA), disabilities, or other special educational needs are advised to contact the instructors at the beginning of the course to arrange teaching and examination methods that, while respecting the learning objectives, take into account individual learning needs and provide appropriate compensatory tools. ASSESSMENT METHODS The written and the oral exam contain exercises and theoretical questions on the topics covered by the teaching, and will require the comprehension and the ability to use the introduced concepts and algorithms. The lab exam will be a guided implementation and use of the algorithms introduced in theoretical classes (notebooks will be used). FURTHER INFORMATION Please contact the instructor for any additional information not included in the teaching description. Agenda 2030 - Sustainable Development Goals Quality education Gender equality