Bayesian optimization (BO) is a powerful class of data-driven techniques for the maximization of expensive-to-evaluate objective functions. These techniques construct a Gaussian process (GP) ...

Abstract: Robust online incremental SLAM applications require metrics to evaluate the impact of current measurements. Despite its prevalence in graph pruning, information-theoretic metrics solely are ...

Master the graphical method of linear programming by understanding how to graph constraints and objective functions, identify feasible regions, and determine optimal solutions efficiently ...

While different objective functions were proposed for different biological systems (Holzhütter, 2004; Price et al, 2004; Knorr et al, 2006), by far the most common assumption is that microbial cells ...

Evaluate the side objective function and filter those candidates that are not satisfied the side_objective function. Here we use side objective function and we want to make sure that the DE performs ...

Optimising wavefunctions by minimising the variance of the energy is actually a very old idea, having being used in the 1930's. The first application using Monte Carlo techniques to evaluate the ...

Optimising wavefunctions by minimising the variance of the energy is actually a very old idea, having being used in the 1930's. The first application using Monte Carlo techniques to evaluate the ...

- Evaluate the objective function (and luckily also the constraints) at the decision variables previously defined. A minimum working example (MWE) of what I want to implement is given in the following ...

We assessed the objective function value of ATP (biomass) yield maximization per flux unit for different ε values according to ε=ε 0 (1±0.5), where ε 0 was set equal to the objective function value, ...