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Finally, we apply our convergence theorem to the convex minimization problem, the problem of finding a zero point of a maximal monotone operator and the complementary problem.
To consider the constraints of the minimization problem, the Lagrange multipliers technique is applied.
By using a dual representation of the minimization problem, the algorithm requires only dot products of the input patterns.
□ The following lemma states that the value of the minimization problem (4) behaves well as the parameter β decreases.
Also, by flipping the sign, the minimization problem can be reformulated to a maximization problem.
An efficient alternating algorithm is proposed for solving the minimization problem of the new object detection model.
As a restriction for the minimization problem, all the forces and moments must be nonnegative.
We say that the minimization problem (1.7) is consistent if the minimization problem (1.7) has a solution.
The solution to the minimization problem can also be written as a function of the kernel function.
The solution to the minimization problem was therefore obtained iteratively using a Newton-type algorithm.
A primal-dual algorithm is used to solve the minimization problem related to the variational regularization.
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