Industrial text analytics for reliability with derivative-free optimization

A Derivative-Free Trust-Region Method for Reliability-Based Optimization

Many engineering problems require to optimize the system performance subject to reliability constraints, and this type of problems are commonly referred to as the reliability based optimization (RBO) problems. In this work we propose a derivativefree trust-region (DF-TR) based algorithm to solve the RBO problems. In particular, we are focused on the type of RBO problems where the objective func...

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, ...

Noisy Derivative-free Optimization with Value Suppression

Derivative-free optimization has shown advantage in solving sophisticated problems such as policy search, when the environment is noise-free. Many real-world environments are noisy, where solution evaluations are inaccurate due to the noise. Noisy evaluation can badly injure derivative-free optimization, as it may make a worse solution looks better. Sampling is a straightforward way to reduce n...