{"id":null,"code":"521392S","name":{"valueFi":"Konveksi optimointi","valueEn":"Convex Optimization","valueSv":""},"credits":7.0,"minCredits":7,"maxCredits":7,"tags":[],"createdAt":1790534825899,"contentList":[{"title":{"valueFi":"Osaamistavoitteet","valueEn":"Learning outcomes","valueSv":""},"content":{"valueFi":"Learning outcomes:\r\n- The students will be able to recognize, formulate, reformulate, and solve various engineering problems as convex optimization problems, both analytically and algorithmically.\r\n- The students will be able to identify convex sets, convex functions, and different types of convex optimization problems.\r\n- The students will learn the necessary and sufficient conditions for optimality as well as the essential concepts of duality.\r\n- The students will be able to write basic MATLAB solvers based on CVX and disciplined convex programming.\r\n- The students will learn high-level algorithmic optimization aspects and will be able to write basic MATLAB solvers.\r\n- The students will be able to recognize the role of convex optimization in various engineering applications from wireless communications, signal processing, and machine learning.\r\n\r\nGeneric skills:\r\n- The students will be able to apply analytical and critical thinking skills in a manner appropriate to their discipline.\r\n- The students will be able to apply creative thinking and problem solving in their work to develop new knowledge and new procedures.","valueEn":"Learning outcomes:\r\n- The students will be able to recognize, formulate, reformulate, and solve various engineering problems as convex optimization problems, both analytically and algorithmically.\r\n- The students will be able to identify convex sets, convex functions, and different types of convex optimization problems.\r\n- The students will learn the necessary and sufficient conditions for optimality as well as the essential concepts of duality.\r\n- The students will be able to write basic MATLAB solvers based on CVX and disciplined convex programming.\r\n- The students will learn high-level algorithmic optimization aspects and will be able to write basic MATLAB solvers.\r\n- The students will be able to recognize the role of convex optimization in various engineering applications from wireless communications, signal processing, and machine learning.\r\n\r\nGeneric skills:\r\n- The students will be able to apply analytical and critical thinking skills in a manner appropriate to their discipline.\r\n- The students will be able to apply creative thinking and problem solving in their work to develop new knowledge and new procedures.","valueSv":""}},{"title":{"valueFi":"Sisältö","valueEn":"Content","valueSv":""},"content":{"valueFi":"- Convex sets.\r\n- Convex functions.\r\n- Convex optimization problems:\r\n> - Linear problems;\r\n> - Quadratic problems;\r\n> - Second-order cone programming;\r\n> - Geometric programming;\r\n> - Semidefinite programming.\r\n- Duality.\r\n- CVX and disciplined convex programming.\r\n- Optimization algorithms:\r\n> - Unconstrained optimization (Newton’s methods);\r\n> - Equality-constrained optimization;\r\n> - Inequality-constrained optimization (interior-point methods).\r\n- Applications:\r\n> - Approximation and fitting;\r\n> - MIMO precoding;\r\n> - Decomposition methods;\r\n> - Sparse and low-rank optimization;\r\n> - Classification and support vector machine.","valueEn":"- Convex sets.\r\n- Convex functions.\r\n- Convex optimization problems:\r\n> - Linear problems;\r\n> - Quadratic problems;\r\n> - Second-order cone programming;\r\n> - Geometric programming;\r\n> - Semidefinite programming.\r\n- Duality.\r\n- CVX and disciplined convex programming.\r\n- Optimization algorithms:\r\n> - Unconstrained optimization (Newton’s methods);\r\n> - Equality-constrained optimization;\r\n> - Inequality-constrained optimization (interior-point methods).\r\n- Applications:\r\n> - Approximation and fitting;\r\n> - MIMO precoding;\r\n> - Decomposition methods;\r\n> - Sparse and low-rank optimization;\r\n> - Classification and support vector machine.","valueSv":""}},{"title":{"valueFi":"Suoritustavat","valueEn":"Study Methods","valueSv":""},"content":{"valueFi":"Teaching methods include face-to-face lectures and self-lectures. Assessment methods include written exam, homework, and final project. Face-to-face lectures and written exams will take place in person at the university; however, some face-to-face lectures may be held online via Zoom in case the teacher is not able to come in person. Attending the course remotely will not be possible.","valueEn":"Teaching methods include face-to-face lectures and self-lectures. Assessment methods include written exam, homework, and final project. Face-to-face lectures and written exams will take place in person at the university; however, some face-to-face lectures may be held online via Zoom in case the teacher is not able to come in person. Attending the course remotely will not be possible.","valueSv":""}},{"title":{"valueFi":"Toteutustavat","valueEn":"Teaching Methods","valueSv":""},"content":{"valueFi":"Teaching methods include face-to-face lectures and self-lectures.\r\n- The first period focuses on the fundamental theoretical aspects and comprises self-lectures and face-to-face lectures (one per week).\r\n- The second period focuses on high-level algorithmic aspects and relevant applications, and mostly comprises face-to-face lectures (two per week).","valueEn":"Teaching methods include face-to-face lectures and self-lectures.\r\n- The first period focuses on the fundamental theoretical aspects and comprises self-lectures and face-to-face lectures (one per week).\r\n- The second period focuses on high-level algorithmic aspects and relevant applications, and mostly comprises face-to-face lectures (two per week).","valueSv":""}},{"title":{"valueFi":"Oppimateriaalit","valueEn":"Learning material","valueSv":""},"content":{"valueFi":"<p>- Book: S. Boyd and L. Vandenberghe, &ldquo;Convex Optimization&rdquo;. Cambridge, U.K., Cambridge Univ. Press, 2004.<br />\r\n- Video lectures by Prof. S. Boyd: available on YouTube: https://www.youtube.com/watch?embed=no&amp;v=McLq1hEq3UY&amp;list=PL3940DD956CDF0622<br />\r\n- Other material by Prof. S. Boyd: available on his web page (e.g., exercises with solutions): http://web.stanford.edu/class/ee364a/<br />\r\n- Other material by the teacher: lecture slides and supporting material.</p>","valueEn":"<p>- Book: S. Boyd and L. Vandenberghe, &ldquo;Convex Optimization&rdquo;. Cambridge, U.K., Cambridge Univ. Press, 2004.<br />\r\n- Video lectures by Prof. S. Boyd: available on YouTube: https://www.youtube.com/watch?embed=no&amp;v=McLq1hEq3UY&amp;list=PL3940DD956CDF0622<br />\r\n- Other material by Prof. S. Boyd: available on his web page (e.g., exercises with solutions): http://web.stanford.edu/class/ee364a/<br />\r\n- Other material by the teacher: lecture slides and supporting material.</p>","valueSv":""}},{"title":{"valueFi":"Lisätiedot","valueEn":"Further information","valueSv":""},"content":{"valueFi":"Timing: next time in Autumn 2026 (periods 1 and 2)\r\n\r\nBasic info: For the academic year 2026/2027, the Convex Optimization course is available for Master’s students (7 ECTS credits) and for doctoral researchers (10 ECTS credits). The course content, face-to-face lectures, self-lectures, homework, and written exams are the same for the 7-ECTS and 10-ECTS versions; a larger and more research-oriented final project is required for the 10-ECTS version.\r\n\r\nTarget group: This course primarily targets ITEE Master’s students and doctoral researchers, but other students from the University of Oulu are also welcome. The course should benefit anyone who uses scientific computing or optimization in engineering and related fields (e.g., communications, signal processing, and machine learning).\r\n\r\nCode of conduct: By taking this course, the students acknowledge the \"Code of conduct for the prevention and processing of misconduct in studies at University of Oulu\" (https://www.oulu.fi/external/Code-of-conduct-for-the-prevention-and-processing-of-misconduct-in-studies-at-University-of-Oulu-2018.pdf).\r\n\r\nUse of AI: Prohibited, not to be used.","valueEn":"Timing: next time in Autumn 2026 (periods 1 and 2)\r\n\r\nBasic info: For the academic year 2026/2027, the Convex Optimization course is available for Master’s students (7 ECTS credits) and for doctoral researchers (10 ECTS credits). The course content, face-to-face lectures, self-lectures, homework, and written exams are the same for the 7-ECTS and 10-ECTS versions; a larger and more research-oriented final project is required for the 10-ECTS version.\r\n\r\nTarget group: This course primarily targets ITEE Master’s students and doctoral researchers, but other students from the University of Oulu are also welcome. The course should benefit anyone who uses scientific computing or optimization in engineering and related fields (e.g., communications, signal processing, and machine learning).\r\n\r\nCode of conduct: By taking this course, the students acknowledge the \"Code of conduct for the prevention and processing of misconduct in studies at University of Oulu\" (https://www.oulu.fi/external/Code-of-conduct-for-the-prevention-and-processing-of-misconduct-in-studies-at-University-of-Oulu-2018.pdf).\r\n\r\nUse of AI: Prohibited, not to be used.","valueSv":""}},{"title":{"valueFi":"Kurssikirjallisuus","valueEn":"Literature","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Esitietovaatimukset","valueEn":"Qualifications","valueSv":""},"content":{"valueFi":"- Required: solid knowledge of linear algebra and MATLAB programming.\r\n- Desirable: course “Introduction to Optimization (031025A)”; exposure to numerical optimization and digital signal processing.","valueEn":"- Required: solid knowledge of linear algebra and MATLAB programming.\r\n- Desirable: course “Introduction to Optimization (031025A)”; exposure to numerical optimization and digital signal processing.","valueSv":""}},{"title":{"valueFi":"Arviointiasteikko","valueEn":"Assessment scale","valueSv":""},"content":{"valueFi":"1-5/HYL","valueEn":"1-5/FAIL","valueSv":"1-5/FAIL"}},{"title":{"valueFi":"Arviointikriteerit","valueEn":"Assessment criteria","valueSv":""},"content":{"valueFi":"0-5","valueEn":"0-5","valueSv":""}},{"title":{"valueFi":"Arviointikriteerit 2","valueEn":"Evaluation criteria 2","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Arviointikriteerit 3","valueEn":"Evaluation criteria 3","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Arviointikriteerit 4","valueEn":"Evaluation criteria 4","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Pääasiallinen opetuskieli","valueEn":"Primary Teaching Language","valueSv":""},"content":{"valueFi":"englanti","valueEn":"English","valueSv":"engelska"}},{"title":{"valueFi":"Taso","valueEn":"Level","valueSv":""},"content":{"valueFi":"Syventävät opinnot","valueEn":"Advanced Studies","valueSv":"Syventävät opinnot"}},{"title":{"valueFi":"Oppiaine","valueEn":"Subject","valueSv":""},"content":{"valueFi":"Tietoliikennetekniikka","valueEn":"Communications Engineering","valueSv":""}},{"title":{"valueFi":"Vastuuhenkilöt","valueEn":"Person in charge","valueSv":""},"content":{"valueFi":"Italo Atzeni","valueEn":"Italo Atzeni","valueSv":"Italo Atzeni"}},{"title":{"valueFi":"Luokittelu","valueEn":"Classification","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Linkit","valueEn":"Links","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}}]}