Section: Overall Objectives

Overall Objectives

In the context of black-box numerical optimization previously described, the main ambition of the RandOpt team is to design and implement novel methods in subdomains with a strong practical demand. Those methods should become future standards that allow to solve important challenging applications in industry or academia. For this, we believe that (i) theory can greatly help for algorithm design; (ii) the development and implementation of proper scientific experimentation methodology is crucial and (iii) it is decisive to provide parameter-less implementations of the methods through open-source software packages. This shapes four main scientific goals for our proposed team:

1. develop novel theoretical frameworks for guiding (a) the design of novel methods and (b) their analysis, allowing to

2. provide proofs of key features of stochastic adaptive algorithms including the state-of-the-art method CMA-ES: linear convergence and learning of second order information.

3. develop novel stochastic numerical black-box algorithms following a principled design in domains with a strong practical need for much better methods namely constrained, multiobjective, large-scale and expensive optimization. Implement the methods such that they are easy to use. And finally, to

4. set new standards in scientific experimentation, performance assessment and benchmarking both for optimization on continuous or combinatorial search spaces. This should allow in particular to advance the state of reproducibility of results of scientific papers in optimization.

All the above relate to our objectives with respect to dissemination and transfer:

1. develop software packages that people can directly use to solve their problems. This means having carefully thought out interfaces, generically applicable setting of parameters and termination conditions, proper treatment of numerical errors, catching properly various exceptions, etc.;

2. have direct collaborations with industrials;

3. publish our results both in applied mathematics and computer science bridging the gap between very often disjoint communities.