3.4 自适应算法的完善(The amendment of the adaptive algorithm) 3.4.1 收敛性条件(Convergence conditions) 3.4.2 自适应速率修正(Modifying the adaptive rate) ..
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与以往的同一领域的经典定理相比，它的收敛性条件是宽松的。
Its convergent condition is relaxed compared with the classical theorem on the same field.
详细分析和论证两个模型的局部超线性收敛性及二次收敛性条件，其中并不需要严格互补条件。
The local superlinear and quadratic convergence of this two models under some mild conditions without the strict complementary condition are analysed and proved.
随后我们提出了求解这类概率约束随机规划的一种近似算法，并在一定的条件下证明了算法的收敛性。
And then, we present an approximation method for solving this probabilistic constrained stochastic programming, and prove certain convergence of the method under some conditions.
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