Now, we deal with the existence of a sequence of weak solutions converging to zero for the problem (P).
Under these hypotheses, we will be able to approximate solutions in the sense of distributions to problem by a sequence of weak solutions to weak problems.
Under certain hypotheses, we approximate solutions in the sense of distributions to problem by a sequence of weak solutions to weak problems.
Accuracy: the boosting algorithm is an ensemble model, which trains a sequence of "weak learners" to gradually achieve a good accuracy.
Next, we will deduce the existence of one solution in the sense of distributions to from the existence of a sequence of weak solutions to.
The overall classifier is fitted to the dataset using a sequence of weak learners, which in this implementation are decision trees.
Physically, this amounts to approximating the input current I by a sequence of weak instantaneous charge injections, arriving at times ( j − 1 / 2 ) Δ, j = 1, 2, 3, … .
Third, we establish the existence of a sequence of weak solutions for the problem (P) converging to zero to obtain the (L^{infty} -bound of weak solutions to the problem (P) based on an iteration method.
The stability means that a sequence of weak solutions to (1.5 - 1.8 1.5 - 1.8e-compact data (rho_{0}), ((rhomathbf {u})_{0}), (mathbf{H}_{0}), and withdmits a subsequence that converges weakly to another solution of the same problem.
Therefore, by Theorem 3.4, we find that problem (3.5) has a sequence of weak solutions which strongly converges to zero in E for all λ ∈ ( λ ¯ 1, λ ¯ 2 ).
For this case, we will prove that problem (1.1) has at least two nonnegative solutions by extracting a minimizing sequence from the Nehari manifold, and we will obtain a sequence of weak solutions with negative energy by the dual fountain theorem.
