Title: Smart Grids and Optimization - A Winning Combination Speaker: Prof. Miguel Anjos Affiliation: Polytechnique Montreal Location: Room 217 Huxley Building Time: 1:30pm (1 hour) Abstract. A smart grid is the combination of a traditional electrical power system with information and energy both flowing back and forth between suppliers and consumers. This new paradigm introduces major challenges such as the integration of intermittent generation and storage, and the need for electricity consumers to play an active role in the operations of…

Title: Stochastic reformulations of linear systems and efficient randomized algorithms Speaker: Dr. Peter Richtarik Affiliation: School of Mathematics, University of Edinburgh Location: Room 217 Huxley Building Time: 3:00pm Abstract. We propose a new paradigm for solving linear systems. In our paradigm, the system is reformulated into a stochastic problem, and then solved with a randomized algorithm. Our reformulation can be equivalently seen as a stochastic optimization problem, stochastically preconditioned linear system, stochastic fixed point problem and as a probabilistic intersection problem. We propose…

Title: Prediction of Stock market crashes,entry exits from bubbles, hedge fund disasters and their prevention Speaker: Prof. William T. Ziemba Affiliation: Sauder School of Business, University of British Columbia Location: LG19b Seminar Room, Business School Time: 2:00pm Abstract. Bubbles occur in financial markets from time to time. By a bubble we mean that the prices are going up just because people expect them to continue rising. In these cases the prices exceed the fair value based on fundamentals. Jarrow and his…

Title: Robust Model Predictive Control Speaker: Dr. Saša V. Raković Location: Room 218 Huxley Building Time: 4:00pm Abstract.  Model predictive control (MPC) is an advanced control technique that employs an open–loop online optimization in order to take account of system dynamics, constraints and control objectives and to obtain the best current control action. Robust MPC (RMPC) is an improved MPC form that is robust against the bounded uncertainty. RMPC employs a generalized prediction framework that allows for a meaningful optimization of,…

Title: Multi-level Optimization by multi-parametric programming & its use for the solution of Mixed Integer Adjustable Robust Optimization Problems Speaker: Prof. Stratos Pistikopoulos, FREng Affiliation: Artie McFerrin Department of Chemical Engineering, Texas A&M University Location: Room 217 Huxley Building Time: 4:00pm Abstract. Optimization problems involving multiple decision makers at different decision levels are referred to as multi-level programming problems. We are considering bi-level (two decision levels) and tri-level (three decision levels) programming problems. Multi-level programming problems are very challenging to solve even…

Title: Multi-level Optimization by multi-parametric programming & its use for the solution of Mixed Integer Adjustable Robust Optimization Problems Speaker: Prof. Michel Gendreau Affiliation: Dept. of Mathematics and Industrial Engineering, Polytechnique Montréal Location: LG19a, Business School Time: 2:00pm Abstract. While Vehicle Routing Problems have now been studied extensively for more than 50 years, those in which some parameters are uncertain at the time where the routes are made have received significantly less attention, in spite of the fact that there are many…

Title: ADMM and Random Walks on Graphs Speaker: Prof. José Bento Affiliation: Dept. of Computer Science, Boston College Location: Room 217 Huxley Building Time: 11:15am - 12:30pm Abstract. A connection between the distributed alternating direction method of multipliers (ADMM) and lifted Markov chains was recently proposed by Franca et al. 2016 for a non-strictly-convex consensus problem parametrized by a graph G. This was followed by a conjecture that ADMM is faster than gradient descent by a square root factor in its convergence…

Title: Large neighbourhood Benders' search Speaker: Dr. Stephen Maher Affiliation: Lancaster University Management School Location: Room 218 Huxley Building Time: 14:00 - 15:30 Abstract. Enhancements for the Benders' decomposition algorithm can be derived from large neighbourhood search (LNS) heuristics. While mixed-integer programming (MIP) solvers are endowed with an array of LNS heuristics, their use is typically limited in bespoke Benders' decomposition implementations. To date, only ad hoc approaches have been developed to enhance the Benders' decomposition algorithm using large neighbourhood search techniques---namely…