These techniques are based on spectral analysis of particular iteration matrices arising in the derivation and aim at optimizing the convergence performance of the method.
It is shown that the condition number has effects on the convergence characteristics and solution qualities, and also that a condition number exists that optimizes the convergence characteristics and solution quality.
This is necessary for an efficient search and a zero penalty at the end of the procedure (this ad hoc scaling factor of 10 was found by optimizing the convergence to a configuration with zero penalty and maximal subset size).
As a byproduct of our investigation, we find that, in general, the controllers which optimize the rate of convergence switch between two linear subsystems, one of which is unstable.
Using the immune genetic algorithm global search optimization ability and good convergence, optimize the weights of the neural network, meanwhile adjusting PID parameters using BP network.
An off-the-shelf L-BFGS [21] package is used to optimize the sparse filtering objective until convergence.
The genetic operators are carefully designed to optimize the neural network, avoiding premature convergence and permutation problems.
The former methods typically involve building a position-weight matrix (PWM) from sequence data, and local search techniques such as expectation maximization or Gibbs sampling are used to optimize the log likelihood ratio until convergence or a maximum number of iterations is reached.
All the atomic positions and lattice parameters were relaxed and optimized until the convergence of force reaches 0.01 eV Å−1.
