What does robust mean in optimization?
Robust optimization is an important subfield of optimization that deals with uncer- tainty in the data of optimization problems.
Under this framework, the objective and constraint functions are only assumed to belong to certain sets in function space (the so-called “uncertainty sets”)..
What is a robust method of optimization?
Robust optimization was relatively recently introduced as a method to incorporate uncertainty into mathematical programming models (Ben-Tal et al., 2009).
The key idea is to hedge the solutions against worst-case realizations of the uncertain parameters..
What is a robust optimization?
Robust optimization is an important subfield of optimization that deals with uncer- tainty in the data of optimization problems.
Under this framework, the objective and constraint functions are only assumed to belong to certain sets in function space (the so-called “uncertainty sets”)..
What is the advantage of robust optimization?
Robust optimization can help eliminate some of the instability/cost associated with the traditional approach by reducing the optimizer's need to trade, as opposed to directly controlling the amount of trading it does..
What is the difference between convex optimization and robust optimization?
We study convex optimization problems for which the data is not specified exactly and it is only known to belong to a given uncertainty set U, yet the constraints must hold for all possible values of the data from U.
The ensuing optimization problem is called robust optimization..
What is the difference between stochastic and robust optimization?
In stochastic optimization, the goal is usually to optimize the expected value of the objective function (min expected cost, max expected profit, etc.).
In robust optimization, because we don't know the probabilities, we instead optimize some other measure..
- In stochastic optimization, the goal is usually to optimize the expected value of the objective function (min expected cost, max expected profit, etc.).
In robust optimization, because we don't know the probabilities, we instead optimize some other measure. - Robust optimization can help eliminate some of the instability/cost associated with the traditional approach by reducing the optimizer's need to trade, as opposed to directly controlling the amount of trading it does.
