2018 Optimization Days
HEC Montréal, Québec, Canada, 7 — 9 May 2018
TB5 Stochastic, robust, and noisy optimization
May 8, 2018 03:30 PM – 05:10 PM
Location: Manuvie (54)
Chaired by Jordan Ninin
4 Presentations

03:30 PM  03:55 PM
Parameters estimation of 3D model for viscoelastic polymers: Bayesian approach
The parameters identification for viscoelastic materials is always open question as the result of illposedness. A Bayes' theorem based statistical method is introduced to select appropriate viscoelastic model and estimate parameters from mechanical test data. The experimental errors are also quantified. The results will be validated by a complex loading test on same sample.

03:55 PM  04:20 PM
Coupling decomposition algorithm with dynamic programming for a stochastic spatial model for mediumterm energy management problem
This talk is on energy management from hydro and thermal sources on a medium time scale with multiple zones with stochastic demand and water inflow. We will discuss the use of decomposition to split the problem spatially, then use dynamic programming to solve the zonal problems. We will compare the results and computation times with the direct solving and will discuss about the complexity growth of the two methods.

04:20 PM  04:45 PM
Solving an inverse integer optimization problem with noisy data using a cutting plane algorithm
This study develops a method for solving an inverse integer optimization problem when solutions are noisy. We propose a cutting plane algorithm to obtain a cost vector for the forward problem such that the given solution becomes optimal or approximately optimal with the minimum optimality gap.

04:45 PM  05:10 PM
Global optimization with quantified constraints and integral objective function: An interval branch and bound approach
The main benefits of IntervalAnalysisbased methods are to cope with nonconvexity and heterogenous optimization problems. We illustrate these approachs by solving a structured robust control problems with H_2 and H_infinity constraint and model uncertainties. These problems can be formulated as an optimization problem with integral objective function and quantified constraints.