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The convergence of the algorithm is guaranteed.
Furthermore, our algorithm is guaranteed to terminate even if the assembly language program is untypeable.
The efficient Viterbi algorithm is guaranteed to find the most probable state path given a sequence and an HMM.
The training algorithm is guaranteed to converge and is very efficient.
The output SNR increases continuously with the number of iterations and the algorithm is guaranteed to converge.
We propose a new family of valid cuts and prove that the algorithm is guaranteed to converge to optimality.
Secondly, the algorithm is guaranteed to give results within a tolerance of the global maximum, and this tolerance may be freely chosen by the analyst.
Provided an initially feasible solution can be found, subsequent feasibility of the algorithm is guaranteed at every update, and the asymptotic stabilization is established.
Moreover, unlike most existing RMPC algorithms, the proposed algorithm is guaranteed to remove steady-state offset in the controlled variables for setpoints (possibly) different from the origin when the system is unknown linear time-invariant.
We provide a new analysis of the original algorithm to derive our own accelerated version, and prove that our algorithm is guaranteed to converge to a stationary point of the reweighted nuclear norm minimization problem.
However, our algorithm is guaranteed to find an approximate solution that conforms to the initialization, which is a desirable property in many applications since the globally optimum solution does not consider any initialization information.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com