FIG. S2 by Bo Guo (1559695)

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FIG. S1 by Bo Guo (1559695)

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Simultaneous Estimation of Many Sparse Networks via Hierarchical Poisson Log-Normal Model by Changhao Ge (22410399)

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The flowchart of Algorithm 2. by Jing Xu (15337)

“…Assuming that stochastic passenger demand follows a specific distribution and considering various constraints, including train capacity, demand, and denied boarding rate constraints, a nonlinear stochastic programming model for joint optimization of overbooking and seat allocation for HSR is constructed with the aim of maximizing railway expected revenue. …”

Objective values of both algorithms for m = 5. by Zhenzhong Zhang (806974)

“…Considering the carbon emission and the uncertainty of customer order arrivals in the actual transportation environment, a stochastic optimization model considering the cost of carbon emission is established with the objective of maximizing the expected profit from order transportation. …”

Objective values of both algorithms for m = 4. by Zhenzhong Zhang (806974)

“…Considering the carbon emission and the uncertainty of customer order arrivals in the actual transportation environment, a stochastic optimization model considering the cost of carbon emission is established with the objective of maximizing the expected profit from order transportation. …”

Biological Function Assignment across Taxonomic Levels in Mass-Spectrometry-Based Metaproteomics via a Modified Expectation Maximization Algorithm by Gelio Alves (51850)

“…To overcome this limitation, we implemented an expectation-maximization (EM) algorithm, along with a biological function database, within the MiCId workflow. …”

HMM to infer selection. by Adam G. Fine (21763286)

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Execution time of the BMDD algorithm. by Huijuan Zhou (5745020)

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Optimization flowchart of dung beetle optimization algorithm. by Xiangfei Tao (20775636)

BOPIM: Bayesian Optimization for Influence Maximization on Temporal Networks by Eric Yanchenko (3266514)

“…For this, we use the Expected Improvement function, suitably adjusting for noise in the observations, and optimize it using a greedy algorithm to account for the cardinality constraint. …”

Changes in railway expected revenue with passenger no-show rate. by Jing Xu (15337)

Variation of railway expected revenue with DB compensation multiplier. by Jing Xu (15337)

Variation of railway expected revenue with maximum DB rate. by Jing Xu (15337)

Comparison of key features of optimization algorithms. by Alireza Norouzpour Shahrbejari (21799964)

Comparison of recovered networks with the Gibbs sampler and the GEM algorithm using credible intervals. by Aapo E. Korhonen (22530674)

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The network recovered from the gut dataset using HMFGraph. by Aapo E. Korhonen (22530674)

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None - Optimization the stochastic optimal reactive power dispatch with renewable energy resources using a modified dandelion algorithm by Naima Agouzoul (21743513)

Comparisons of median computation times (in seconds) based on 5 runs for the GEM algorithm and the MCMC methods. by Aapo E. Korhonen (22530674)

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P-values and frequency trajectories around significant SNPs. by Adam G. Fine (21763286)

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