Course Description:
Study of the theory and algorithms used to solve optimization problems
in both deterministic and stochastic settings, with an emphasis on
the latter. Topics include
duality theory and descent methods in deterministic optimization;
stochastic approximation, motivated by the need to optimize
in the presence of noisy measurements;
simulated annealing, motivated by the global optimization problem;
and the theory of optimal transportation, an important example
of infinite-dimensional optimization problems.
Familiarity with stochastic processes (e.g., STAT 551b) is assumed.
Knowledge of ordinary differential equations and real analysis is recommended.
Course Website:
Log on to the Classes.v2 server (Yale only).
Last modified on September 15, 2007