ZOOpt: a toolbox for derivative-free optimization

Derivative Free Optimization

Derivative-Free Optimization

In many engineering applications it is common to find optimization problems where the cost function and/or constraints require complex simulations. Though it is often, but not always, theoretically possible in these cases to extract derivative information efficiently, the associated implementation procedures are typically non-trivial and time-consuming (e.g., adjoint-based methodologies). Deriv...

Derivative free optimization method

Derivative free optimization (DFO) methods are typically designed to solve optimization problems whose objective function is computed by a “black box”; hence, the gradient computation is unavailable. Each call to the “black box” is often expensive, so estimating derivatives by finite differences may be prohibitively costly. Finally, the objective function value may be computed with some noise, ...

Objective-derivative-free methods for constrained optimization

We propose feasible descent methods for constrained minimization that do not make explicit use of objective derivative information. The methods at each iteration sample the objective function value along a finite set of feasible search arcs and decrease the sampling stepsize if an improved objective function value is not sampled. The search arcs are obtained by projecting search direction rays ...

Derivative-Free Robust Optimization for Circuit Design

In this paper, we introduce a framework for derivative-free robust optimization based on the use of an efficient derivative-free optimization routine for mixed integer nonlinear problems. The proposed framework is employed to find a robust optimal design of a particular integrated circuit (namely a DC-DC converter commonly used in portable electronic devices). The proposed robust optimization a...