ADVANCED ALGORITHMS

Course objectives

General goals: This class will present algorithms and data structures for solving complex problems. Specific goals: Applying knowledge and understanding: Students will acquire the ability of detecting the mathematical properties of problems, and of determining which techniques should be used to solve it. Critical and judgmental abilities: Students will be able to determine which approaches can be used to solve a variety of algorithmic problems. Communication skills: Students will be able to present algorithmic ideas, and to explain properties of various algorithmic problems. Ability of learning: Students will be able to think algorithmically.

Channel 1
FLAVIO CHIERICHETTI Lecturers' profile

Program - Frequency - Exams

Course program
The class will cover several advanced algorithmic techniques. Optimization Algorithms: - greedy algorithms (10 hours) - LP-based algorithms (10 hours) - SDP-based algorithms (5 hours) - Submodular Optimization (5 hours) Large-Scale Data Algorithms: - graph analysis (5 hours) - Clustering (10 hours) - LSH (5 hours) - algoritmi online (10 hours)
Prerequisites
Students will need to be able to understand mathematical proofs, and to have basic knowledge of algorithmics, probability, and combinatorics.
Books
Algorithm Design (Kleinberg / Tardos).
Teaching mode
Lectures.
Frequency
Attendance is not mandatory, but it is strongly recommended.
Exam mode
Each exam will be made up of a written test (with open-ended, and close-ended, questions), to be finished in roughly 2 hours, on the topics presented in the class.
Lesson mode
Lectures.
  • Lesson code1047613
  • Academic year2025/2026
  • CourseComputer Science
  • CurriculumSingle curriculum
  • Year1st year
  • Semester2nd semester
  • SSDINF/01
  • CFU6