Cubic regularizationFilter methodsSequential quadratic programmingGlobal convergenceIn this paper, we propose a filter sequential adaptive regularization algorithm using cubics (ARC) for solving nonlinear equality constrained optimization. Similar to sequential quadratic programming methods, an ARC subproblem with ...
Algorithm 1: AdaCN. Require:ng //Mini-batch size Require:η //Stepsize Require:β1, β2∈ [0,1)//Parameters of exponential moving average Require:ε, ρ, θ//Positive parameters Require:x0, g0, B0, m0, V0//Initialize variables as zero vectors or ...
In order to have a comprehensive performance evaluation, we begin the optimization of the above models with five different sizes of initial datasets (N = 2, 5, 10, 15, 20) that are uniformly sampled from the search space14. As for eachNand each algorithm, the results are ba...
2. 自适应线性 §1.2自适应线性(adaptive linear)感知器 §1.3 madaline网络 §1.4 bp网络 §1.5 bp网络的应用 习题 第二章 联想记忆神经 … product.china-pub.com|基于2个网页 3. 线性适配倒角 我有我的个性~`~忽忽 ... Normal bevel 法线倒角Adaptive linear线性适配倒角Adaptive cubic 立方适配倒角 ......
The proposed adaptive image interpolation algorithm is illustrated in Fig. 1. Within the proposed approach, a low-resolution (decimated) image frame is first interpolated by using the cubic B-spline function into a coarsely up-sampled image frame, which is then partitioned into non-overlapping, ...
They used the E[I] algorithm for the selection and ranking and their work shows that to find a globally optimum LED, striking a delicate balance between exploration and exploitation is favored in selecting new sample points. This trade-off serves to improve the global accuracy of the model, ...
For an application in the adaptive MOL context, the basic algorithm must be modified as the space grid may change from step to step. As the problem is stiff, derivative information from only the right endpoint tR is used. As, in general, a matrix B may appear in the left hand side of...
Furthermore, the learning algorithm used uses a hybrid optimization technique that was put to use during the 100-epoch training of the fuzzy inference system (FIS). The processing settings are shown in Table 6, which includes the learning and membership function parameters for the data processing...
Zhang, L., Wu, X.: An edge-guided image interpolation algorithm via directional filtering and data fusion. IEEE Trans. Image Process. 15(8), 2226–2238 (2006) Article Google Scholar Zhang, M., Desrosiers, C.: High-quality image restoration using low-rank patch regularization and global ...
They are aimed at improving the data term to make the algorithm more resistant to noise [16,17], more robust under illumination changes [18–20], and more capable to deal with large displacements [10,1,21]. In addition, refining the smoothness term to preserve motion discontinuity [22]. ...