Convergence curve of unimodal functions compared with the meta-heuristic algorithms. by Yong Gao (29929)

Distribution of objective function values solved by three algorithms. by Meilin Zhu (688698)

Python-based Monte Carlo Simulation Tool by Yangfan Jiang (9372782)

The SCOPer algorithm can be run efficiently on multiple cores. by Nima Nouri (2183760)

One iteration of the NSGA-II algorithm. by Erick Axel Padilla-García (16839233)

ADT: A Generalized Algorithm and Program for Beyond Born–Oppenheimer Equations of “<i>N</i>” Dimensional Sub-Hilbert Space by Koushik Naskar (7510592)

“…It is noteworthy to mention that the nonadiabatic coupling terms (NACTs) often become singular (removable) at degenerate point(s) or along a seam in the nuclear configuration space (CS) and thereby, a unitary transformation is required to convert the kinetically coupled (adiabatic) Hamiltonian to a potentially (diabatic) one to avoid such singularity­(ies). The “ADT” program can be efficiently used to (a) formulate analytic functional forms of differential equations for ADT angles and diabatic potential energy matrix and (b) solve the set of coupled differential equations numerically to evaluate ADT angles, residue due to singularity­(ies), ADT matrices, and finally, diabatic potential energy surfaces (PESs). …”

Flowchart of the general algorithm for one trial. by Junying Wang (509009)

Average quality scores of the models of the analysis set as a function of the <i>w</i>_<i>SP</i> with which the DOPE potential has been included in the objective function... by Giacomo Janson (8138517)

CPU runtime of each algorithm: (a) Running Time Comparison of Different Algorithms on F1-F10 and (b) Running Time Comparison of Different Algorithms on F11-F20. by Ruyi Dong (9038174)

Prediction performance of different optimization algorithms. by Ali-Kemal Aydin (10968731)

“…<p>(A) 3 algorithms were compared in terms of the residuals of the cost function of the optimized TF on 7 mice datasets (Derivative free algorithm failed in optimizing a TF in a mouse). …”

Continuous Probability Distributions generated by the PIPE Algorithm by LUIS G.B. PINHO (14073372)

Hypothetical example illustrating how the <i>search</i>.<i>conv</i> function algorithm works. by Silvia Castiglione (5010581)

Line charts of the elbow method and the k-value selection algorithm: (A) elbow method; (B) k-value selection algorithm. by Jingnan Yan (20727331)

Subjects:

a) FO Function, b) FI function. by Huang Jiexian (17060975)

“…The SRL32 primitive (Reconfigurable Look up Tables—RLUTs) and DPR (Dynamic Partial Reconfiguration) are employed to reconfigure single round MISTY1 / KASUMI algorithms on the run-time. The RLUT based architecture attains dynamic logic functionality without extra hardware resources by internally modifying the LUT contents. …”

Iteration curve of each algorithm: (a) Convergence curves of the average best fitness for functions F1-F10, (b) Convergence curves of the average best fitness for functions F11-F20 and (c) Correspondence between curve colors and algorithms. by Ruyi Dong (9038174)

“…<p>Iteration curve of each algorithm: (a) Convergence curves of the average best fitness for functions F1-F10, (b) Convergence curves of the average best fitness for functions F11-F20 and (c) Correspondence between curve colors and algorithms.…”

<b>Gene Catalog and Associated Functional Data</b> for BioProject PRJNA1311452 by Satheeswaran Thangaraj (22187269)

Pipeline of the interactor algorithm. by Jose Cleydson F. Silva (8685594)

Verification of the in-line PS function. by Raul Castañeda (13932971)

Experiment 1b: Evaluating modularity-based consensus clustering pipelines on the algorithm design datasets as a function of the mixing parameter. by Yasamin Tabatabaee (18095491)

Restoration process via Algorithm 1. by Dilber Uzun Ozsahin (18320075)