Scaling Bayesian optimization to high dimensions is challenging task as the global optimization of high-dimensional acquisition function can be expensive and often infeasible. Existing methods depend either on limited “active” variables or the additive form of the objective function. We propose a new method for high-dimensional Bayesian optimization, that uses a dropout strategy to optimize onl...

Contextual policy search allows adapting robotic movement primitives to different situations. For instance, a locomotion primitive might be adapted to different terrain inclinations or desired walking speeds. Such an adaptation is often achievable by modifying a relatively small number of hyperparameters; however, learning when performed on an actual robotic system is typically restricted to a ...

This article introduces a model-based reinforcement learning (RL) approach for continuous state and action spaces. While most RL methods try to find closed-form policies, the approach taken here employs numerical on-line optimization of control action sequences. First, a general method for reformulating RL problems as optimization tasks is provided. Subsequently, Particle Swarm Optimization (PS...

9 A new method to generate optimal structured residuals from partial principal component models is introduced. The models are 10 selected according to a pre-designed fault-to-residual structure matrix. The structures are so chosen that a certain degree of freedom 11 is left to allow optimization. The performance measure for optimization is the ratio of the fault-gain to the noise standard devia...

Preliminary layout design of buildings has a substantial effect on the ultimate design of structural components and accordingly influences the construction cost. Exploring structurally efficient forms and shapes during the conceptual design stage of a project can also facilitate the optimum integrated design of buildings. This paper presents an automated method of determining column layout desi...

Derivative-free optimization methods are suitable for sophisticated optimization problems, while are hard to scale to high dimensionality (e.g., larger than 1,000). Previously, the random embedding technique has been shown successful for solving high-dimensional problems with low effective dimensions. However, it is unrealistic to assume a low effective dimension in many applications. This pape...

A new method to generate optimal structured residuals from partial principal component models is introduced. The models are selected according to a pre-designed fault-to-residual structure matrix. The structures are so chosen that a certain degree of freedom is left to allow optimization. The performance measure for optimization is the ratio of the fault-gain to the noise standard deviation in ...

a computational optimization was performed for a direct-injection diesel engine using three-dimensional modeling. fully transient cfd analyses of different valve profile strategies for the intake and compression strokes were performed to evaluate the effects on both engine performance and in-cylinder flow-field evolution. the turbulence model was used along with the second order linear upwind s...

The paper presents an orthogonally-anchored adaptive linear interference rejection technique in space-time rake processing. With the use of a super short training sequence, the initial space-time adaptive filtering vector composed of two mutually orthogonal components is calculated by using a projection-based filter optimization technique. The adaptive filter optimization based on the preoptimi...

In this paper we do a systematic investigation of continuous methods for pixel, line pixel and line dejittering. The basis for these investigations are the discrete line dejittering algorithm of Nikolova and the partial differential equation of Lenzen et al for pixel dejittering. To put these two different worlds in perspective we find infinite dimensional optimization algorithms linking to the...