Derivative-free optimization methods

Derivative-Free Optimization Via Proximal Point Methods

Derivative-Free Optimization (DFO) examines the challenge of minimizing (or maximizing) a function without explicit use of derivative information. Many standard techniques in DFO are based on using model functions to approximate the objective function, and then applying classic optimization methods on the model function. For example, the details behind adapting steepest descent, conjugate gradi...

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 ...

Methods for Network Optimization and Parallel Derivative-free Optimization

This thesis is divided into two parts that each is concerned with a specific problem. The problem under consideration in the first part is to find suitable graph representations, abstractions, cost measures and algorithms for calculating placements of unmanned aerial vehicles (UAVs) such that they can keep one or several static targets under constant surveillance. Each target is kept under surv...

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...