Combining Fairness with Throughput: Online Routing with Multiple Objectives

Combining Fairness with Throughput : Online Routing with Multiple

Online Stochastic Optimization with Multiple Objectives

In this paper we propose a general framework to characterize and solve the stochastic optimization problems with multiple objectives underlying many real world learning applications. We first propose a projection based algorithm which attains an O(T−1/3) convergence rate. Then, by leveraging on the theory of Lagrangian in constrained optimization, we devise a novel primal-dual stochastic approx...

Combining Multiple Heuristics Online

Heuristics for solving NP-hard problems often have complementary strengths and weaknesses. For example, algorithms developed by statistical physicists are extremely fast at solving satisfiable random Boolean formulae, while algorithms based on chronological backtracking are faster on unsatisfiable and structured formulae. What if we could combine several heuristics into a meta-heuristic with be...

Markov Automata with Multiple Objectives

Markov automata combine non-determinism, probabilistic branching, and exponentially distributed delays. This compositional variant of continuous-time Markov decision processes is used in reliability engineering , performance evaluation and stochastic scheduling. Their verification so far focused on single objectives such as (timed) reachability, and expected costs. In practice, often the object...

Scheduling the Brazilian Soccer Tournament with Fairness and Broadcast Objectives

The Brazilian soccer tournament is organized every year by the Brazilian Soccer Confederation. Its major sponsor is TV Globo, the largest media group and television network in Brazil, who imposes constraints on the games to be broadcast. Scheduling the games of this tournament is a very constrained problem, with two objectives: breaks minimization (fairness) and the maximization of the revenues...