Sentence examples for proving that the algorithm from inspiring English sources

This section aims at proving that the algorithm converges in the realistic case (i.e., using an a priori unknown channel covariance matrix) if a relevant initialization σ ̂ ( i = 0 ) 2 is chosen.

For a class of Genetic Algorithms, we provide theoretical underpinning of a class of empirically derived results, by proving that the algorithms degenerate to randomised, cost-independent search as mutation probabilities increase.

We prove that the algorithm achieves constant message and linear time complexity.

Meanwhile, we prove that the algorithm converges with probability one, which means that an accurate clock synchronization is achieved.

We prove that the algorithm always succeeds in constructing a Lyapunov function if the system possesses an exponentially stable equilibrium.

We propose a new family of valid cuts and prove that the algorithm is guaranteed to converge to optimality.

We prove that the algorithm achieves a linear speed-up over the left-to-right algorithm on uniform AND/OR trees when the number of processors used is close to the height of the input tree.

The theoretical analysis and simulation results prove that the algorithm scales well with the network size, and can obtain near-optimal in-time delivery of pieces.

We also analyze the probability distribution of outputs, using the notion of Shannon entropy, and prove that the algorithm is somewhat close to any "ideal" equidistributed algorithm.

It is proven that the algorithm ends up to be a model-free iterative algorithm to solve the GARE of the linear quadratic discrete-time zero-sum game.

A set of optimisation trials is accomplished to prove that the algorithm is valid for determining the optimum values of the manipulator design parameters.