We study three wave function optimization methods based on energy minimization in a variational Monte Carlo framework: the Newton, linear, and perturbative methods. In the Newton method, the parameter variations are calculated from the energy gradient and Hessian, using a reduced variance statistical estimator for the latter. In the linear method, the parameter variations are found by diagonali...

The eld of time series analysis and forecasting methods has signi cantly changed in the last decade due to the in uence of new knowledge in non-linear dynamics. New methods such as arti cial neural networks replaced traditional approaches which usually were appropriate for linear models only. Nevertheless, there are still applications where accurate estimations of linear processes, such as ARMA...

Chemotaxis is the process by which cells behave in a way that follows the chemical gradient. Applications to bacteria growth, tissue inflammation and vascular tumours provide a focus on optimization strategies. Experiments can characterize the form of possible chemotactic sensitivities. This paper addresses the recovery of the chemotactic sensitivity from these experiments while allowing for no...

An optimal layout synthesis methodology for CMOS MEMS accelerometers is presented. It consists of a parametrized layout generator that optimizes design objectives while meeting functional specifications. The behavior of the device is estimated using lumped parameter analytical equations. The design problem is then formulated into a non-linear constrained optimization problem. Such an approach t...

We discuss system identification of self-organizing, swarm robotic systems using a “gray-box” approach, based on probabilistic macroscopic models. Using a well known case study concerned with the autonomous inspection of a regular structure by a swarm of miniature robots, we show how to achieve highly accurate predictive models by combining previously developed probabilistic modeling and calibr...

In [1], with the evidence framework, the almost inversely linear dependency between the optimal parameter r in norm-r support vector regression machine r-SVR and the Gaussian input noise is theoretically derived. When r takes a non-integer value, r-SVR cannot be easily realized using the classical QP optimization method. This correspondence attempts to achieve two goals: (1) The Newton-decent-m...