Best convergence curves of the algorithms with respect to iteration number. by Erdal Eker (19251018)

Tuning of hyper-parameters in each iteration for AMO. by Shehr Bano (20421706)

Tuning of hyper-parameters in each iteration for AMP. by Shehr Bano (20421706)

Iteration diagram of genetic algorithm. by Ke Peng (2220973)

The relaxed gradient based iterative algorithm for solving the generalized coupled complex conjugate and transpose Sylvester matrix equations by Yanping Long (10171727)

“…(Journal of the Franklin Institute, 2018), we adopt relaxation technique and introduce relaxation factors into the gradient based iterative (GI) algorithm, and the relaxed based iterative (RGI) algorithm is established to solve the generalized coupled complex conjugate and transpose Sylvester matrix equations. …”

More Numerical Precision for Less Compute Cost: Optimizing a Local Correlation Algorithm for Second Order Møller–Plesset Theory and Comparing against Pair Natural Orbital Methods... by Zhenling Wang (306377)

More Numerical Precision for Less Compute Cost: Optimizing a Local Correlation Algorithm for Second Order Møller–Plesset Theory and Comparing against Pair Natural Orbital Methods... by Zhenling Wang (306377)

The optimized algorithm based on machine learning for inverse kinematics of two painting robots with non-spherical wrist by Xiaoqi Wang (164823)

Iterative Registration and Clustering (IREC) Algorithm: The input image stack (<i>S</i>) is first aligned and grouped using registration and clustering (REC) based on an initial re... by Creighton M. Lisowski (22756444)

Wrapper-based feature selection methods. by Xiaohua Zeng (793632)

Performance comparison of four optimization methods. by Xuexing Du (20624042)

“…The relative error is calculated as , where <b>x</b>_<i>o</i> is the target solution and is the estimation. The algorithms stop when the relative error falls below 0.01; P-SBI achieves a solution with a relative error less than 0.01 in just 3 iterations.…”

datasheet1_Graph Neural Networks for Maximum Constraint Satisfaction.pdf by Jan Tönshoff (10192709)

Video1_Reducing the muscle activity of walking using a portable hip exoskeleton based on human-in-the-loop optimization.AVI by Linghui Xu (6295400)

“…However, a lengthy period (several hours) of continuous walking for iterative optimization for each individual makes it less practical, especially for disabled people, who may not endure this process. …”

Online sextuplet mining for SNAP-loss. by Muhammad Usman Tariq (11022141)

Description of the real-world dataset. by Fadi K. Dib (5204807)

“…Unlike most population-based methods, Jaya algorithm is a parameter-less algorithm in that it requires no algorithm-specific control parameters and only population size and number of iterations need to be specified, which makes it easy for researchers to apply in the field. …”

Screenshot of our visualization tool MGDrawVis. by Fadi K. Dib (5204807)

“…Unlike most population-based methods, Jaya algorithm is a parameter-less algorithm in that it requires no algorithm-specific control parameters and only population size and number of iterations need to be specified, which makes it easy for researchers to apply in the field. …”

Steps in the extraction of 14 coordinates from the CT slices for the curved MPR. by Linus Woitke (22783534)

“…The image is then cleaned in c) using morphological filtering with an <i>opening</i> operation to remove small-scale noise. …”

PathOlOgics_RBCs Python Scripts.zip by Ahmed Elsafty (16943883)

“…</p><p dir="ltr">In terms of classification, a second algorithm was developed and employed to preliminary sort or group the individual cells (after excluding the overlapping cells manually) into different categories using five geometric measurements applied to the extracted contour from each binary image mask (see PathOlOgics_script_2; preliminary shape measurements). …”

Triad and tetrad descriptions with corresponding hierarchy types. by Travis R. Moore (19532635)

Thesis-RAMIS-Figs_Slides by Felipe Santibañez-Leal (10967991)

“…Importantly, this strategy locates samples adaptively on the transition between facies which improves the performance of conventional \emph{<i>MPS</i>} algorithms. In conclusion, this work shows that preferential sampling can contribute in \emph{<i>MPS</i>} even at very small sampling regimes and, as a corollary, demonstrates that prior models (obtained form a training image) can be used effectively not only to simulate non-sensed variables of the field, but to decide where to measure next.…”