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11 results for “combinatorial optimization”
Data for "Quantum combinatorial optimization beyond the variational paradigm: simple schedules for hard problems"
<p>Contains instances of combinatorial optimizations problems (Sherrington-Kirkpatrick and MAX 2-SAT) as well as further results and plotting notebooks for the paper "Quantum combinatorial optimization beyond the variational paradigm: simple schedules for hard problems".</p>
Data for GECCO2023 Paper "Many-objective (Combinatorial) Optimization is Easy"
<p><strong>Data for Paper "Many-objective (Combinatorial) Optimization is Easy"</strong></p> <ul> <li><strong>instances.tar.xz</strong> contains 𝜌mnk-landscape instances</li> <li><strong>metrics.csv</strong> contains the metric-values based on full enumeration</li> <li><strong>performance.csv</strong> contains the Pareto resolution and the hypervolume of the different algorithms one each instance</li> <li><strong>performance_neval.csv</strong> contains the number of evaluations performed by PLS</li> <li><strong>script.R</strong> is the R script for producing the figures</li> </ul> <p><strong>Reference</strong></p> <p>Arnaud Liefooghe and Manuel López-Ibáñez. 2023. Many-objective (Combinatorial) Optimization is Easy. In Genetic and Evolutionary Computation Conference (GECCO '23), July 15–19, 2023, Lisbon, Portugal. <a href="https://doi.org/10.1145/3583131.3590475">https://doi.org/10.1145/3583131.3590475</a></p> <p><strong>Abstract</strong></p> <p>It is a common held assumption that problems with many objectives are harder to optimize than problems with two or three objectives. In this paper, we challenge this assumption and provide empirical evidence that increasing the number of objectives tends to reduce the difficulty of the landscape being optimized. Of course, increasing the number of objectives brings about other challenges, such as an increase in the computational effort of many operations, or the memory requirements for storing non-dominated solutions. More precisely, we consider a broad range of multi- and many-objective combinatorial benchmark problems, and we measure how the number of objectives impacts the dominance relation among solutions, the connectedness of the Pareto set, and the landscape multimodality in terms of local optimal solutions and sets. Our analysis shows the limit behavior of various landscape features when adding more objectives to a problem. Our conclusions do not contradict previous observations about the inability of Pareto-optimality to drive search, but we explain these observations from a different perspective. Our findings have important implications for the design and analysis of many-objective optimization algorithms.</p>
Benchmark Instances for Robust Combinatorial Optimization with Budgeted Uncertainty
<p>We provide test instances for robust combinatorial optimization with budget uncertainty in the objective function.<br> The set contains nominal problems from the MIPLIB 2017 that have been converted into robust problems and instances of the robust knapsack problem. Both problem sets have been described and used for benchmarking in the paper "A Branch & Bound Algorithm for Robust Binary Optimization with Budget Uncertainty", published in Mathematical Programming Computation by Christina Büsing, Timo Gersing and Arie Koster.<br> Furthermore, we provide instances for robust weighted matching on bipartite graphs and robust weighted independent set. The latter are based on graphs for the clique problem of the second DIMACS implementation challenge (1993). Both problem sets have been described and used for benchmarking in the paper "Recycling Inequalities for Robust Combinatorial Optimization with Budget Uncertainty", presented at IPCO 2023 by the same authors.</p> <p> </p> <p>Paper "A Branch & Bound Algorithm for Robust Binary Optimization with Budget Uncertainty": <a href="https://doi.org/10.1007/s12532-022-00232-2"> https://doi.org/10.1007/s12532-022-00232-2</a><br> Paper "Recycling Inequalities for Robust Combinatorial Optimization with Budget Uncertainty": <a href="https://doi.org/10.1007/978-3-031-32726-1_5">https://doi.org/10.1007/978-3-031-32726-1_5</a><br> For algorithms solving these problems see: <a href="https://doi.org/10.5281/zenodo.7463371">https://doi.org/10.5281/zenodo.7463371</a></p>
Dynamic combinatorial optimization of in vitro and in vivo heparin antidotes
<p>This folder contains selected molecular dynamics simulations movies for the paper " Dynamic combinatorial optimization of <em>in vitro</em> and <em>in vivo</em> heparin antidotes"</p>
Dataset for Comparing Three Generations of D-Wave Quantum Annealers for Minor Embedded Combinatorial Optimization Problems
<p>Dataset for the paper titled "Comparing Three Generations of D-Wave Quantum Annealers for Minor Embedded Combinatorial Optimization Problems"</p> <p>LA-UR-23-20367</p>
Generating Optimal Robust Continuous Piecewise Linear Regression with Outliers Through Combinatorial Benders Decomposition - Data Sets
<p>Data Sets for the Paper: "Generating Optimal Robust Continuous Piecewise Linear Regression with Outliers Through Combinatorial Benders Decomposition".</p>
Optimized CRISPR-Cas12a platform for combinatorial drop-out genetic screening (RNA-seq)
GEO Series GSE141130. Mus musculus. 29 samples. Type: Expression profiling by high throughput sequencing.
An optimized protocol for single cell transcriptional profiling by combinatorial indexing
GEO Series GSE186824. Mus musculus. 334 samples. Type: Expression profiling by high throughput sequencing.
Combinatorial Extracellular Matrix Tissue Chips for Optimizing Mesenchymal Stromal Cell Microenvironment and Manufacturing
GEO Series GSE288678. Homo sapiens. 24 samples. Type: Expression profiling by high throughput sequencing.
Optimized metrics for combinatorial and orthogonal CRISPR screens
GEO Series GSE215175. Homo sapiens. 20 samples. Type: Other.
Combinatorial optimization of mRNA structure, stability, and translation for RNA-based therapeutics
GEO Series GSE173083. synthetic construct; Homo sapiens. 70 samples. Type: Other.
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OpenNeuro
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