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An optimization criterion is formulated as the maximum of the buckling pressure.
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At first glance, this can be formulated as the problem of maximum weighted matching (i.e., pairing) in bipartite graphs, and any of the assignment algorithms, such as the well-known Hungarian algorithm [24], seem to be a candidate solution.
Introducing the constraint that the Euclidean norm of the uncertain load set is fixed, the robust compliance minimization problem is formulated as the minimization of the maximum eigenvalue of the aggregated symmetric matrix according to the Rayleigh Ritz theorem for symmetric matrices.
A feasible answer could be formulated as the following.
Then, the association of the action paths is formulated as a maximum set coverage problem with the results of action detection as a priori.
By assuming that the original image can be described by means of an MRF, the above problem is formulated as a maximum a posteriori estimation problem.
The hard segmentation step is formulated as a Maximum A Posterior (MAP) estimation problem, where we iteratively perform depth refinement and bi-layer classification to achieve optimal results.
The integrated task of tracking and recognition is formulated as a maximum a posteriori estimation problem.
For a sequence of states,, and frame parameters (where is a vector containing both the position and velocity at time ) this can be formulated as a maximum likelihood problem: (4).
Two optimization problems are formulated as minimizing maximum per-user mean square error (MSE) and sum MSE with the per-transmitter power constraint.
Formulated as a maximum a posteriori probability (MAP) problem, a multi-cue functional is minimized by a data-driven Markov chain Monte Carlo (DDMCMC) process that arrives at a solution that is shown empirically to be near the global minimum.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com