# Optimization training

## SCILAB training course for optimization

You might not be familiar with the numerical concept of optimization, but I am pretty sure your daily work requires you to:

• Build something that is more and more efficient / Increase your profits
• While limiting the production & operation costs / Limiting the associated risk   Managers Operational assistance Maximize efficiency, lower costs Traders Decision support system Maximize revenue, lower risks Engineers Multidisciplinary & robust design optimization / Model Predictive Control

If you are still reading, I can say: I told you! And what you are currently facing is called an optimization problem: It is all about trying to maximize one or many objectives under given constraints.

## Training objective

Provide you with the basics to be able to:

1. Recognize what in your daily work is an optimization problem
2. Understand some basics on optimization theory and its numerical implementation
3. Qualify your porblem to choose the most efficient method to solve it
4. Discover different methods implemented in Scilab and make efficient use of it

## Training content

 What is optimization used for Introduction to optimization problem in real life Optimal control Optimal design Inverse problem Numerical setting Introduction to optimization problem in mathematics & numeric Optimization problem formulation Equivalent formulation (e.g. Dual) Classic solving process Different solving methods (direct search vs differential calculus) Problem qualification Introduction to problem segmentation and problematic of solver efficiency Discrete/Continuous Simple or multi-objective (Non)Smooth (Un)Constrained (Non)Linear Algorithms in SCILAB Detailed description of each kind of problem with classic use-cases, and dedicated SCILAB function to solve it. Exercises are provide to learn how to handle those functions Linear Quadratic Semi definite Non-linear least squares Non-linear Multi-objective Discrete Non-smooth Robust optimization Introduction to the reliability issue and need for robust optimization Sensitivity analysis Reliability analysis Robust design optimization

## Training duration Hugues-A. GARIOUD In charge of R&D at SCILAB Simulation, Optimization & Statistics email: