PhD School Lecture, 10/02/2021, 09:00 - 10:45


Stefano Lucidi

Department of Computer, Control and Management Engineering

University "La Sapienza" of Roma


Derivative-free methods for black-box optimization problems

Many real world problems  can be modeled as  black-box optimization problems. This class of optimization problems is characterized by the fact that the values of the functions describing the problems  (objective functions and   constraints) are given by a “black box”  such as, for example, a complex simulation program or direct measurements. Since any analytical representation of the problem is  not available, the first order derivatives (or more in general the first order  information) of the objective function and  constraints can be neither calculated nor approximated explicitly. This has motivated an  increasing interest  toward the definition and the study (derivative-free) methods able to tackle such a class of difficult optimization problems by employing only the information deriving from the function values.  . These methods can be divided into two main classes.
-The “global” derivative free methods which aims to get a global minimum point   of the given black box optimization problem.  
-The “local” derivative free which produce sequences of points “converging” towards stationary points of the considered minimization problem.
This talk considers a  particular subclass of “local” derivative free  methods: the linesearch derivative free methods. These methods have the common feature of enforce their  convergence properties by using linesearch techniques along suitable search directions.
First  the main ideas and results  of these methods are  recalled. Then  linesearch derivative free  algorithms for different classes of nonlinear  optimization problems  are described.

 Lecture Lucidi - Derivative-free methods for BB opt

 Video Lecture Lucidi 


Stefano Lucidi is Full Professor in the University of Rome La Sapienza.

He teaches Operations Research in the first level degrees in Engineering Management and Continuous  Optimization  in the second level degrees in Engineering Management. His research  interests are mainly focused on the study, definition of nonlinear optimization methods and their applications in several different fields such as: machine learning, optimal design, management of healthcare services.

The papers produced  by his  research can be found in

He is founding partner of “ACTOR SRL. Analytics, Control Technologies and Operations Research” Spin-Off company of the University of Rome "La Sapienza".

He is member of the Editorial Board of the international journal 4OR-A Quarterly Journal of Operations Research.



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