Introduction to convex optimization

In this thesis, we touched upon the concept of convexity which is one of the essential topics in optimization. There exist many real world problems that mathematically modelling these problems and trying to solve them are the focus point of many researchers. Many algorithms are proposed for solving such problems. Almost all proposed methods are very efficient when the modelled problems are convex. Therefore, convexity plays an important role in solving those problems. There are many techniques that researchers use to convert a non-convex model to a convex one. Also, most of the algorithms that are suggested for solving non-convex problems try to utilize the notions of convexity in their procedures. In this work, we begin with important definitions and topics regarding convex sets and function. Next, we will introduce optimization problems in general, then, we will discuss convex optimization problems and give important definitions in relation with the topic. Furthermore, we will touch upon Linear Programming which is one of the most famous and useful cases of Convex Optimization problems. Finally, we will discuss the Generalized Inequalities and their application in vector optimization problem

A compact optimization model for the tail assignment problem

International audienceThis paper investigates a new model for the so-called Tail Assignment Problem, which consists in assigning a well-identified airplane to each flight leg of a given flight schedule, in order to minimize total cost (cost of operating the flights and possible maintenance costs) while complying with a number of operational constraints. The mathematical programming formulation proposed is compact (i.e., involves a number of 0−1 decision variables and constraints polynomial in the problem size parameters) and is shown to be of significantly reduced dimension as compared with previously known compact models. Computational experiments on series of realistic problem instances (obtained by random sampling from real-world data set) are reported. It is shown that with the proposed model, current state-of-the art MIP solvers can efficiently solve to exact optimality large instances representing 30-day flight schedules with typically up to 40 airplanes and 1500 flight legs connecting as many as 21 airports. The model also includes the main existing types of maintenance constraints, and extensive computational experiments are reported on problem instances of size typical of practical applications