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This solves the optimization problem of worst-case performance by maximizing the approximate negentropy subject to the SOCP constraint.
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This section introduces an algorithm that solves the optimization problem modelled in the previous section using a dynamic programming approach that results in a generalization of the Viterbi algorithm.
This approach solve the optimization problem with fixed LR.
This involves solving the optimization problem min E 1 ( D h, P 1 ( N ) > D m a x ) | h ∈ ℛ o u t ( N ) × Pr ( h ∈ ℛ o u t ( N ) ) s.t.
To obtain this figure, we solve the optimization problem (18a)–(18c) without the constraint (18c) and plot the optimal values of the objective function (18a), the minimum, and the maximum powers.
Therefore, they are used in this paper to solve the optimization problem.
In this subsection, we solve the optimization problem (13) in the simplest case that the idle period is uniform distribution.
In this section, we solve the optimization problem (13) in the case that the idle period is generalized Pareto distribution.
In this subsection, we solve the optimization problem (13) in the case that the idle period is exponential distribution.
In this paper, to solve the optimization problem of completely trained boosted cascade detector on false-classified instances, we propose a new base hypothesis weight optimization algorithm called DOOMRED (Direct Optimization Of Margin for Rare Event Detection) using a mathematically derived error upper bound of boosting algorithms.
This temporal coupling requires solving the optimization problem for whole time horizon as a unique large scale optimization problem, increasing the number of variables and constraints, and therefore the computation time.
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CEO of Professional Science Editing for Scientists @ prosciediting.com