Alternate Minimization and Scaling Algorithms - Theory, Applications and Connections Across Mathematics and Computer Science

This course covers the theory, applications, and connections of Alternate Minimization and Scaling Algorithms across Mathematics and Computer Science. The learning outcomes include understanding the P vs NP problem, generalization, analysis, quantum leap, and Gibbs Sampling. The course teaches alternate minimization and scaling algorithms, along with when they work and their sources. The teaching method involves lectures and theoretical explanations. The intended audience for this course includes students and professionals interested in theoretical computer science and mathematics.