- Learn and know how to put into practice resolution methods of optimization problems that are large scale due to uncertainties and to temporal and spatial dimensions
Researchers and engineers working in the field of energy, transport, finance, etc.
Attendees are invited to download and fill out the survey from our web site to adapt the programme to their expectations.
Knowledge of continuous optimization (linear programming, convexity, duality) and probability calculus (random variable, conditional expectation)
Sensors and data abound. This spatial and temporal information is supposed to allow a better management in new energy systems, transport or eco-industrial parks, to quote a few examples. This leads to problems of large-scale optimization, the formulation of which is delicate. Indeed, one needs to take into account at least three dimensions: temporal (stages of decision, dynamics, inertia), spatial (different decision units connected by flows), stochastic (various scenarios are possible) and thus risk. This leads to multi-stage stochastic optimization problems which enter the class of large-scale systems.
- Stochastic programming: from one-stage to multistage problems
- Stochastic optimal control and dynamic programming
- Stochastic Dual Dynamic Programming Algorithm
- Advanced decomposition methods
- Practical works in Julia
- Interactive session on industry case studies
Detailed programme available on CIRM website.
This 4-day training session alternates courses, computing and interactive case studies sessions.
Attendees should bring their own laptop, equipped with the free software Julia. Computers will be also available onsite for those in need.
P. Carpentier, J.-P. Chancelier, M. De Lara and V. Leclère (Professors)