15th EUROPT Workshop on Advances in Continuous Optimization
Montréal, Canada, 12 — 14 juillet 2017
15th EUROPT Workshop on Advances in Continuous Optimization
Montréal, Canada, 12 — 14 juillet 2017
Derivative Free Optimization 1
13 juil. 2017 11h30 – 12h45
Salle: Nancy et MichelGaucher
Présidée par Delphine Sinoquet
3 présentations

11h30  11h55
A new variable selection strategy for the parallel space decomposition in derivativefree optimization.
The current parallel space decomposition of the Mesh Adaptive Direct Search algorithm (PSDMADS) is an asynchronous parallel method that uses a simple generic strategy to decompose a problem into smaller dimension subproblems. The present work explores new strategies for selecting the subset of variables defining subproblems to be explored in parallel. These strategies are based on ranking the variables using statistical tools to determine the most influential ones. The statistical approach improves the decomposition of the problem into smaller more relevant subproblems. This work aims to improve the use of available processors.

11h55  12h20
A Trust Region Method for Solving DerivativeFree Problems with Binary and Continuous Variables Part 1: the underlying algorithm
Trust region methods are used to solve various blackbox optimization problems, especially when no derivative information is available. In this talk, we will consider an extension of trust region methods for mixedinteger nonlinear programming (MINLP). There are both theoretical and computational innovations to handle the binary variables, including restricting the quadratic model, solving mixed integer quadratic problems and handling wellpoisedness. Whereas, of necessity, we address globality with respect to the binary variables, we are content to obtain good local minima for the continuous variables, at least in part because our typical context involves expensive simulations.

12h20  12h45
A Trust Region Method for Solving DerivativeFree Problems with Binary and Continuous Variables  Part 2: applications in the energy domain
Optimization takes place in many IFPEN applications: inferring the parameters of numerical models from experimental data (earth sciences, combustion in engines, chemical process), design optimization (wind turbine, risers, networks of oil pipelines), optimizing the settings of experimental devices (calibration of engines, catalysis). These typically require minimizing a functional that is complex (nonlinearities, depending on mixed continuous and integer/discrete variables) and expensive to estimate (solution of a numerical model based on differential systems), and for which derivatives are often not available.
In this talk, we illustrate the potential of the proposed trust region method adapted to binary and continuous variables on several applications in the energy domain.