QMC calculations crucially depend on the quality of the trial-function, and so it is essential to have an optimized wave-function as close as possible to the ground state.

The problem of function optimization is a very important research topic in numerical simulation. In QMC, in addition to the usual difficulties to find the minimum of multidimensional parametric function, the statistical noise is present in the estimate of the cost function (usually the energy), and its derivatives
, required for an efficient optimization.

Different cost functions and different strategies were used to optimize a many-body trial-function.
Usually three cost functions were used in QMC optimization energy, variance or a linear combination of them. In this thesis we always used energy optimization. The variance optimization have the advantage to be bounded by below, to be positive defined and its minimum is known, but different authors Ref. (40,41,42) recently showed that the energy optimization is
more effective than the variance one.

There are different motivations for this:
first, usually one is interested in the lowest energy
rather than in the lowest variance in both variational and diffusion Monte Carlo; second, variance optimization takes many iterations to optimize determinant parameters and often the optimization can get stuck in multiple local minimum and it suffers of the "false convergence" problem (41);
third energy-minimized wave functions on average yield more
accurate values of other expectation values than variance
minimized wave functions do (40).

The optimization strategies can be divided into three categories. The first strategy is based on correlated sampling together with deterministic optimization methods (43). Even if this idea yielded very accurate results for the first-row atoms (43), this procedure can have problems if parameters affect the nodes, and moreover density ratio of the current and initial trial-function increases exponentially with the size of the system (44). In the second strategy one use a large bin to evaluate the cost function and its derivatives in such way that the noise can be neglected and deterministic methods can be used (see for instance (45,46)).

Third approach, the one we used, is based on an iterative technique to handle directly with noise functions. The first example of these methods is the so called Stochastic Gradient Approximation (SGA) Ref. (47), recently used also for structure optimization Ref. (48).

In this thesis we have used two new optimization methods the Stochastic Reconfiguration (SR) method (49,15) and Stochastic Reconfiguration with Hessian acceleration (SRH) (50).