Past Events

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: 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: 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: 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: 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: On Bit Representations of Mixed-Integer Quadratic Programs. Speaker: Prof. Adam Letchford Affiliation: Management School - Lancaster University Location: Room 217 Huxley Building Time: 3:00pm (1 hour) Abstract. A standard trick in integer programming is to replace each bounded general-integer variable with a small number of binary variables, using the bit representation of the given variable. (See, e.g., Owen & Mehrotra, 2002; Coppersmith & Lee, 2005; Muldoon et al., 2013; Bonami & Margot, 2015). Recently, bit representation was found to be…

Title: Solution of an old problem in vector optimisation - Support function of the generalised Jacobian Speaker: Prof. Abbas Edalat Affiliation: Department of Computing - Imperial College Location: Room 217 Huxley Building Time: 3:00pm (1 hour) Abstract. Many optimisation problems are non-smooth in the sense that the objective function is not differentiable. This is invariably the case in many areas of application when the objective function contains simple constructs such as maximum or minimum of several differentiable functions or contains, for example, the absolute…

Title: On Robust Selection Problems Speaker: Marc Goerigk Affiliation: Department of Management Science - Lancaster University Location: Room LT1 Business School Time: 2:30pm (1 hour) Abstract. Robust optimisation considers problems that are affected by uncertain data: How can we find a solution that performs well, even if things don't go quite as planned? Typically, adding robustness to a problem makes it harder to solve. The selection problem is maybe the simplest non-trivial combinatorial optimisation problem. Given a set of n…