Sampling for data freshness optimization: Non-linear age functions

Freshness-Aware Thompson Sampling

To follow the dynamicity of the user’s content, researchers have recently started to model interactions between users and the ContextAware Recommender Systems (CARS) as a bandit problem where the system needs to deal with exploration and exploitation dilemma. In this sense, we propose to study the freshness of the user’s content in CARS through the bandit problem. We introduce in this paper an ...

Comparative Study of Particle Swarm Optimization and Genetic Algorithm Applied for Noisy Non-Linear Optimization Problems

Optimization of noisy non-linear problems plays a key role in engineering and design problems. These optimization problems can't be solved effectively by using conventional optimization methods. However, metaheuristic algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) seem very efficient to approach in these problems and became very popular. The efficiency of these ...

Experiments for Non - Linear Functions

Optimization of Non-linear Functions Using Evolutionary Computation

Global optimization is important if the function has a number of optima of which some are local and some global. Since many real world problems contain multiple optima, traditional methods may not be adequate to solve such problems due to the possibility of getting trapped at local optimum. Hence they do not ensure global optima. Differential Evolution (DE) is an evolutionary optimization techn...

Adaptive Sampling Probabilities for Non-Smooth Optimization

Abstract Standard forms of coordinate and stochastic gradient methods do not adapt to structure in data; their good behavior under random sampling is predicated on uniformity in data. When gradients in certain blocks of features (for coordinate descent) or examples (for SGD) are larger than others, there is a natural structure that can be exploited for quicker convergence. Yet adaptive variants...