Threshold Dynamics Method: Theories, Algorithms, and Applications / Wang, Dong, author.

Multiple Testing and False Discovery Rate Control: Theory, Methods and Algorithms / Chen, Shiyun

Optimization over Digraphs: Linear Algorithms with Linear Convergence / Xin, Ran, author.

Variational Models with Elastica Energies: A Comparison, a New Model and New Algorithms / He, Xuan

Performance Comparison of Imputation Algorithms on Missing at Random Data / Addo, Evans Dapaa

Self-polar Polytopes and Sequential Importance Sampling Algorithms / Jensen, Alathea, author

On the Efficiency of Algorithms for Tensor Decompositions and Their Applications / Rouse, Kathryn

-of-the-art algorithms on large, ill-conditioned problems. We discuss applications of the method to electronic structure

algorithms which outperformed the base learners when compared using standard evaluation metrics. The results

quantum-safe not much is known of what a quantum attack would look like on these algorithms. Specifically

compared in simulation setting. An R package named TensorRegression was built to present the algorithms

bins. This process eliminates the need for data dimension reduction algorithms. The method of "pseudo

To begin, a discussion of known effective and efficient algorithms for computing constellations

algorithms, we measure accuracy by using the misclassification or error rate of each algorithm. Additionally

computation. Many well-known algorithms including Fast Fourier transform and Walsh transform turned out to be

-interval multi-objective linear programming (MOLPs) algorithms are presented in Chapter 4. Numerical examples are

networks of arbitrary sizes, we could design two algorithms based on the intuition to find mappings between

to numerical algorithms for solving practical optimization problems with large size of datasets. One

algorithms are indispensable. A highly parallel solution strategy using different solvers for different