2017 (15)
Advancing Cross-Cutting Ideas for Computational Climate Science. Evans, K.; Ng, E. G.; Caldwell, P.; Hoffman, F. M.; Jackson, C.; Kleese Van Dam, K.; Leung, L.; Martin, D.; Ostrouchov, G.; Tuminaro, R.; Ullrich, P.; Wild, S. M.; and Williams, S. Technical Report U.S.\ Department of Energy, BER and ASCR, January 2017.
Advancing Cross-Cutting Ideas for Computational Climate Science [pdf]Paper   doi   bibtex
Derivative-Free Robust Optimization By Outer Approximations. Menickelly, M.; and Wild, S. M. Technical Report ANL/MCS-P9004-1017, Argonne National Laboratory, Mathematics and Computer Science Division, 2017.
Derivative-Free Robust Optimization By Outer Approximations [pdf]Paper   bibtex
Bragg Coherent Modulation Imaging: Strain- and Defect-Sensitive Single Views of Extended Samples. Ulvestad, A.; Cha, W.; Calvo-Almazan, I.; Maddali, S.; Wild, S. M.; Maxey, E.; Dupraz, M.; and Hruszkewycz, S. O. Technical Report Argonne National Laboratory, Mathematics and Computer Science Division, 2017.
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Solving Derivative-Free Nonlinear Least Squares Problems with POUNDERS. Wild, S. M. In Terlaky, T.; Anjos, M. F.; and Ahmed, S., editor(s), Advances and Trends in Optimization with Engineering Applications, pages 529–540. SIAM, 2017.
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Analysis and Correlation of Application I/O Performance and System-Wide I/O Activity. Madireddy, S.; Balaprakash, P.; Carns, P.; Latham, R.; Ross, R.; Snyder, S.; and Wild, S. M. In 2017 International Conference on Networking, Architecture, and Storage (NAS), pages 1–10, August 2017. IEEE
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CONORBIT: Constrained Optimization by Radial Basis Function Interpolation in Trust Regions. Regis, R.; and Wild, S. M. Optimization Methods and Software, 32(3): 552–580. 2017.
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Joint Reconstruction of X-Ray Fluorescence and Transmission Tomography. Di, Z. W.; Chen, S.; Hong, Y. P.; Jacobsen, C.; Leyffer, S.; and Wild, S. M. Optics Express, 25(12): 13107–13124. May 2017.
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Understanding and Improving the Trust in Results of Numerical Simulations and Scientific Data Analytics. Cappello, F.; Gupta, R.; Di, S.; Constantinescu, E. M; Peterka, T.; and Wild, S. M In 10th Workshop on Resilience in High Performance Computing, 2017. Clusters, Clouds and Grids, in conjunction with 23rd International European Conference on Parallel and Distributed Computing (Euro-Par)
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Asynchronously Parallel Optimization Solver for Finding Multiple Minima. Larson, J.; and Wild, S. M. Mathematical Programming Computation. 2017. To appear
Asynchronously Parallel Optimization Solver for Finding Multiple Minima [pdf]Paper   doi   bibtex
Improving FPGA Design Parameter Exploration: Timing, Power, and Area. Mametjanov, A.; Balaprakash, P.; Choudary, C.; Hovland, P. D.; Wild, S. M.; Sabin, G.; and Wolfe, G. Technical Report ANL/MCS-P7000-0117, Argonne National Laboratory, Mathematics and Computer Science Division, January 2017.
Improving FPGA Design Parameter Exploration: Timing, Power, and Area [pdf]Paper   bibtex
Machine Learning Based Parallel I/O Predictive Modeling: A Case Study on Lustre File Systems. Madireddy, S.; Balaprakash, P.; Carns, P.; Latham, R.; Ross, R.; Snyder, S.; and Wild, S. M. Technical Report ANL/MCS-P9021-1017, Argonne National Laboratory, Mathematics and Computer Science Division, October 2017.
Machine Learning Based Parallel I/O Predictive Modeling: A Case Study on Lustre File Systems [pdf]Paper   bibtex
Accurate, Rapid Identification of Dislocation Lines in Coherent Diffractive Imaging via a Min-Max Optimization Formulation. Ulvestad, A.; Menickelly, M.; and Wild, S. M. Technical Report Argonne National Laboratory, Mathematics and Computer Science Division, 2017.
Accurate, Rapid Identification of Dislocation Lines in Coherent Diffractive Imaging via a Min-Max Optimization Formulation [link]Paper   bibtex
Uncertainty Quantification for Optical Model Parameters. Lovell, A. E.; Nunes, F. M.; Sarich, J.; and Wild, S. M. Physical Review C, 95(2): 024611. February 2017.
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Manifold Sampling for Nonconvex Optimization of Piecewise Linear Compositions. Khan, K.; Larson, J.; and Wild, S. M. Technical Report ANL/MCS-P8001-0817, Argonne National Laboratory, MCS Division, August 2017.
Manifold Sampling for Nonconvex Optimization of Piecewise Linear Compositions [pdf]Paper   bibtex
Computing Just What You Need: Online Data Analysis and Reduction at Extreme Scales. Foster, I.; Ainsworth, M.; Allen, B.; Bessac, J.; Cappello, F.; Choi, J. Y.; Constantinescu, E.; Davis, P. E.; Di, S.; Di, W.; Guo, H.; Klasky, S.; Van Dam, K. K.; Kurc, T.; Liu, Q.; Malik, A.; Mehta, K.; Mueller, K.; Munson, T.; Ostouchov, G.; Parashar, M.; Peterka, T.; Pouchard, L.; Tao, D.; Tugluk, O.; Wild, S.; Wolf, M.; Wozniak, J. M.; Xu, W.; and Yoo, S. In Rivera, F. F.; Pena, T. F.; and Cabaleiro, J. C., editor(s), European Conference on Parallel Processing, pages 3–19, 2017. Springer International Publishing
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  2016 (17)
Report of the DOE Workshop on Management, Analysis, and Visualization of Experimental and Observational Data – The Convergence of Data and Computing. Bethel, E. W.; and Greenwald (eds.), M. Technical Report U.S.\ Department of Energy, ASCR, 2016.
Report of the DOE Workshop on Management, Analysis, and Visualization of Experimental and Observational Data – The Convergence of Data and Computing [pdf]Paper   bibtex
AutoMOMML: Automatic Multi-Objective Modeling with Machine Learning. Balaprakash, P.; Tiwari, A.; Wild, S. M.; Carrington, L.; and Hovland, P. D. In Kunkel, M. J.; Balaji, P.; and Dongarra, J., editor(s), High Performance Computing: 31st International Conference, ISC High Performance 2016, Frankfurt, Germany, June 19-23, 2016, Proceedings, pages 219–239, 2016. Springer
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Doing Moore with Less – Leapfrogging Moore's Law with Inexactness for Supercomputing. Leyffer, S.; M.~Wild, S.; Fagan, M.; Snir, M.; Palem, K.; Finkel, H.; and Yoshii, K. Technical Report ANL/MCS-P6077-1016, Argonne National Laboratory MCS, 2016.
Doing Moore with Less – Leapfrogging Moore's Law with Inexactness for Supercomputing [link]Paper   bibtex
Exploiting Performance Portability in Search Algorithms for Autotuning. Roy, A.; Balaprakash, P.; Hovland, P. D.; and Wild, S. M. In 2016 IEEE International Parallel and Distributed Processing Symposium IPDPS Workshops, pages 1535–1544, 2016. Eleventh International Workshop on Automatic Performance Tuning (iWAPT2016)
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Rejoinder: Modeling an Augmented Lagrangian for Blackbox Constrained Optimization. Gramacy, R. B.; Gray, G. A.; Le~Digabel, S.; Lee, H. K. H.; Ranjan, P.; Wells, G.; and Wild, S. M. Technometrics, 58(1): 26–29. 2016.
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Novel Large Scale Simulation Process to Support DOT's CAFE Modeling System. Moawad, A.; Balaprakash, P.; Rousseau, A.; and Wild, S. M. International Journal of Automotive Technology, 17(6): 1067–1077. 2016.
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Origins and Optimization of Entanglement in Plasmonically Coupled Quantum Dots. Otten, M.; Larson, J.; Min, M.; Wild, S. M.; Pelton, M.; and Gray, S. K. Physical Review A, 94(2): 022312. 2016.
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Management, Analysis, and Visualization of Experimental and Observational Data – The Convergence of Data and Computing. Bethel, E. W.; Greenwald, M.; Kleese van Dam, K.; Parashar, M.; Wild, S. M.; and Wiley, H. S. In 2016 IEEE 12th International Conference on e-Science, pages 213–222, 2016.
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Bayesian Optimization under Mixed Constraints with a Slack-Variable Augmented Lagrangian. Picheny, V.; Gramacy, R. B.; Wild, S. M.; and Le~Digabel, S. In Advances in Neural Information Processing Systems (NIPS), pages 1435–1443, December 2016.
Bayesian Optimization under Mixed Constraints with a Slack-Variable Augmented Lagrangian [link]Paper   bibtex
Optimization-Based Approach for Joint X-Ray Fluorescence and Transmission Tomographic Inversion. Di, Z. W.; Leyffer, S.; and Wild, S. M. SIAM Journal on Imaging Sciences, 9(1): 1–23. 2016.
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Overcoming the Power Wall by Exploiting Application Inexactness and Emerging COTS Architectural Features: Trading Precision for Improving Application Quality. Fagan, M.; Schlachter, J.; Yoshii, K.; Leyffer, S.; Palem, K.; Snir, M.; M.~Wild, S.; and Enz, C. In 29th IEEE International System-on-Chip Conference (SOCC), pages 241–246, 2016.
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Single-View Phase Retrieval of an Extended Sample by Exploiting Edge Detection and Sparsity. Tripathi, A.; McNulty, I.; Munson, T.; and Wild, S. M. Optics Express, 24(21): 24719–24738. 2016.
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Coherent Diffractive Imaging of Time-Evolving Samples with Improved Temporal Resolution. Ulvestad, A.; Tripathi, A.; Hruszkewycz, S. O.; Cha, W.; Fuoss, P. H.; Wild, S. M.; and Stephenson, G. B. Physical Review B, 93(18): 184105. May 2016.
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Manifold Sampling for L1 Nonconvex Optimization. Larson, J.; Menickelly, M.; and Wild, S. M. SIAM Journal on Optimization, 26(4): 2540–2563. 2016.
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A Batch, Derivative-Free Algorithm for Finding Multiple Local Minima. Larson, J.; and Wild, S. M. Optimization and Engineering, 17(1): 205–228. 2016.
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Modeling an Augmented Lagrangian for Blackbox Constrained Optimization. Gramacy, R. B.; Gray, G. A.; Le~Digabel, S.; Lee, H. K. H.; Ranjan, P.; Wells, G.; and Wild, S. M. Technometrics, 58(1): 1–11. 2016.
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Non-Negative Matrix Analysis in X-Ray Spectromicroscopy: Choosing Regularizers. Mak, R.; Wild, S. M.; and Jacobsen, C. AIP Conference Proceedings, 1696: 020034. 2016. XRM 2014: Proceedings of the 12th International Conference on X-Ray Microscopy
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  2015 (18)
A Bayesian Approach for Parameter Estimation and Prediction using a Computationally Intensive Model. Higdon, D.; McDonnell, J. D.; Schunck, N.; Sarich, J.; and Wild, S. M. Journal of Physics G: Nuclear and Particle Physics, 42(3): 034009. 2015.
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Quantification of Uncertainties in Nuclear Density Functional Theory. Schunck, N.; McDonnell, J. D.; Sarich, J.; Wild, S. M.; and Higdon, D. Nuclear Data Sheets, 123: 115–118. 2015.
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Uncertainty Quantification and Propagation in Nuclear Density Functional Theory. Schunck, N.; McDonnell, J. D.; Higdon, D.; Sarich, J.; and Wild, S. M. European Physical Journal A, 51(12): 169(1–14). 2015.
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Error Analysis in Nuclear Density Functional Theory. Schunck, N.; McDonnell, J. D.; Sarich, J.; Wild, S. M.; and Higdon, D. Journal of Physics G: Nuclear and Particle Physics, 42(3): 034024. 2015.
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One-Nucleon Transfer Reactions and the Optical Potential. Nunes, F. M.; Lovell, A.; Ross, A.; Titus, L. J.; Charity, R. J.; Dickhoff, W. H.; Mahzoon, M. H.; Sarich, J.; and Wild, S. M. In Proceedings, 14th International Conference on Nuclear Reaction Mechanisms: Varenna, Italy, June 15-19, 2015, pages 123–130, 2015.
One-Nucleon Transfer Reactions and the Optical Potential [pdf]Paper   bibtex
Statistical Uncertainties of a Chiral Interaction at Next-to-Next-To Leading Order. Ekström, A.; Carlsson, B. D.; Wendt, K.; Forssén, C.; Hjorth-Jensen, M.; Machleidt, R.; and Wild, S. M. Journal of Physics G: Nuclear and Particle Physics, 42(3): 034003. 2015.
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Nuclear Energy Density Optimization: UNEDF2. Kortelainen, M.; McDonnell, J.; Nazarewicz, W.; Olsen, E.; Reinhard, P.; Sarich, J.; Schunck, N.; Wild, S.; Davesne, D.; Erler, J.; and Pastore, A. In JPS Conference Proceedings, volume 6, pages 020018, 2015. Proceedings of the Conference on Advances in Radioactive Isotope Science (ARIS2014)
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Collective I/O Tuning Using Analytical and Machine Learning Models. Isaila, F.; Balaprakash, P.; Wild, S. M.; Kimpe, D.; Latham, R.; Ross, R.; and Hovland, P. In Cluster Computing (CLUSTER), 2015 IEEE International Conference on, pages 128–137, September 2015.
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Dynamic Model-Driven Parallel I/O Performance Tuning. Behzad, B.; Byna, S.; Wild, S. M.; Prabhat; and Snir, M. In Cluster Computing (CLUSTER), 2015 IEEE International Conference on, pages 184–193, September 2015.
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Using Multi-Objective Optimization for HEV Component Sizing. R.~Vijayagopal; P.~Sharer; S.~M.~Wild; A.~Rousseau; R.~Chen; S.~Bhide; G.~Dongarkar; M.~Zhang; and R.~Meier In Proceedings of the International Electric Vehicle Symposium and Exhibition, May 2015. Paper number EVS28_0153:D3-03
Using Multi-Objective Optimization for HEV Component Sizing [pdf]Paper   bibtex
Uncertainty Quantification for Nuclear Density Functional Theory and Information Content of New Measurements. McDonnell, J. D.; Schunck, N.; Higdon, D.; Sarich, J.; Wild, S. M.; and Nazarewicz, W. Physical Review Letters, 114(12): 122501. 2015.
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ACCOLADES: A Scalable Workflow Framework for Large-Scale Simulation and Analyses of Automotive Engines. Aithal, S. M.; and Wild, S. M. In Kunkel, J. M.; and Ludwig, T., editor(s), High Performance Computing, volume 9137, of Lecture Notes in Computer Science, pages 87–95, 2015. Springer International Supercomputing Conference – High Performance (ISC-HPC 2015)
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Analysis of the Tradeoffs between Energy and Run Time for Multilevel Checkpointing. Balaprakash, P.; Bautista Gomez, L. A.; Bouguerra, M.; Wild, S. M.; Cappello, F.; and Hovland, P. D. In Jarvis, S. A.; Wright, S. A.; and Hammond, S. D., editor(s), High Performance Computing Systems. Performance Modeling, Benchmarking, and Simulation, volume 8966, of Lecture Notes in Computer Science, pages 249–263. Springer, 2015.
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Derivative-Free Optimization for Parameter Estimation in Computational Nuclear Physics. Wild, S. M.; Sarich, J.; and Schunck, N. Journal of Physics G: Nuclear and Particle Physics, 42(3): 034031. 2015.
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A Taxonomy of Constraints in Black-Box Simulation-Based Optimization. Le~Digabel, S.; and Wild, S. M. Technical Report ANL/MCS-P5350-0515, Argonne National Laboratory, Mathematics and Computer Science Division, January 2015.
A Taxonomy of Constraints in Black-Box Simulation-Based Optimization [pdf]Paper   bibtex
Development of a Reduced-Order Design/Optimization Tool for Automotive Engines Using Massively Parallel Computing. Aithal, S. M.; and Wild, S. M. In Proceedings of the SAE 12th International Conference on Engines & Vehicles, 2015. SAE SAE Technical Paper 2015-24-2390
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Visualizing and Improving the Robustness of Phase Retrieval Algorithms. Tripathi, A.; Leyffer, S.; Munson, T.; and Wild, S. M. Procedia Computer Science, 51: 815–824. 2015. International Conference on Computational Science, (ICCS) 2015
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Autotuning FPGA Design Parameters for Performance and Power. Mametjanov, A.; Balaprakash, P.; Choudary, C.; Hovland, P. D.; Wild, S. M.; and Sabin, G. In Proceedings of the 23rd IEEE International Symposium on Field-Programmable Custom Computing Machines (FCCM 2015), pages 84–91, May 2015.
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  2014 (10)
Energy-Performance Tradeoffs in Multilevel Checkpoint Strategies (Poster). Bautista Gomez, L.; Balaprakash, P.; Bouguerra, M.; Wild, S.; Cappello, F.; and Hovland, P. In 2014 IEEE International Conference on Cluster Computing (CLUSTER), pages 278–279, September 2014.
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Empirical Performance Modeling of GPU Kernels Using Active Learning. Balaprakash, P.; Rupp, K.; Mametjanov, A.; Gramacy, R. B.; Hovland, P. D.; and Wild, S. M. In Bader, M.; Bode, A.; Bungartz, H.; Gerndt, M.; Joubert, G.; and Peters, F., editor(s), Parallel Computing: Accelerating Computational Science and Engineering (CSE), volume 25, of Advances in Parallel Computing, pages 646–655. IOS Press, 2014. International Conference on Parallel Computing (ParCo2013)
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SciDAC: Accelerating Scientific Discovery, Transforming Computational Science. D'Azevedo, E.; Ng, E. G.; and Wild, S. M. SIAM News, 47(3). 2014.
SciDAC: Accelerating Scientific Discovery, Transforming Computational Science [link]Paper   bibtex
Unsupervised Cell Identification on Multidimensional X-ray Fluorescence Datasets. Wang, S.; Ward, J.; Leyffer, S.; Vogt, S.; Wild, S. M.; and Jacobsen, C. Journal of Synchrotron Radiation, 21(3): 568–579. 2014.
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Improving Parallel I/O Autotuning with Performance Modeling. Behzad, B.; Byna, S.; Wild, S. M.; Prabhat; and Snir, M. In Proceedings of the 23rd International Symposium on High-Performance Parallel and Distributed Computing (HPDC14), pages 253–256, 2014. ACM
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Applied Mathematics Research for Exascale Computing. Dongarra, J.; Hittinger, J.; Bell, J.; Chacón, L.; Falgout, R.; Heroux, M.; Hovland, P.; Ng, E.; Webster, C.; and Wild, S. Technical Report U.S.\ Department of Energy, ASCR, March 2014.
Applied Mathematics Research for Exascale Computing [pdf]Paper   doi   bibtex
Non-Negative Matrix Analysis for Effective Feature Extraction in X-ray Spectromicroscopy. Mak, R.; Lerotic, M.; Fleckenstein, H.; Vogt, S.; Wild, S. M.; Leyffer, S.; Sheynkin, Y.; and Jacobsen, C. Faraday Discussions, 171: 357–371. 2014.
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Multi-Objective Optimization of HPC Kernels for Performance, Power, and Energy. Balaprakash, P.; Tiwari, A.; and Wild, S. M. In Jarvis, S. A.; Wright, S. A.; and Hammond, S. D., editor(s), High Performance Computing Systems. Performance Modeling, Benchmarking and Simulation, volume 8551, of Lecture Notes in Computer Science, pages 239–260, 2014. Springer
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Nuclear Energy Density Optimization: Shell Structure. Kortelainen, M.; McDonnell, J.; Nazarewicz, W.; Olsen, E.; Reinhard, P.; Sarich, J.; Schunck, N.; Wild, S.; Davesne, D.; Erler, J.; and Pastore, A. Physical Review C, 89(5): 054314. 2014.
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Do You Trust Derivatives or Differences?. Moré, J. J.; and Wild, S. M. Journal of Computational Physics, 273: 268–277. 2014.
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  2013 (12)
Framework for Optimizing Power, Energy, and Performance (Poster). Balaprakash, P.; Tiwari, A.; and Wild, S. M. In International Conference for High Performance Computing, Networking, Storage, and Analysis (SC13), November 2013.
Framework for Optimizing Power, Energy, and Performance (Poster) [link]Paper   bibtex
Unsupervised Cell Identification on Multidimensional X-ray Fluorescence Datasets. Wang, S.; Ward, J.; Leyffer, S.; Wild, S. M.; Jacobsen, C.; and Vogt, S. In Proceedings of ACM SIGGRAPH 2013 Posters, 2013. Article No.~88
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Active-Learning-Based Surrogate Models for Empirical Performance Tuning. Balaprakash, P.; Gramacy, R.; and Wild, S. M. In Proceedings of IEEE International Conference on Cluster Computing (CLUSTER 2013), pages 1–8, September 2013.
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An Experimental Study of Global and Local Search Algorithms in Empirical Performance Tuning. Balaprakash, P.; Wild, S. M.; and Hovland, P. D. In High Performance Computing for Computational Science - VECPAR 2012, 10th International Conference, Kobe, Japan, July 17-20, 2012, Revised Selected Papers, volume 7851, of Lecture Notes in Computer Science, pages 261–269. Springer, 2013.
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Axially Deformed Solution of the Skyrme-Hartree-Fock-Bogolyubov Equations Using the Transformed Harmonic Oscillator Basis (II) HFBTHO v2.00d: A New Version of the Program. Stoitsov, M. V.; Schunck, N.; Kortelainen, M.; Michel, N.; Nam, H.; Olsen, E.; Sarich, J.; and Wild, S. M. Computer Physics Communications, 184(6): 1592–1604. 2013.
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Non-Intrusive Termination of Noisy Optimization. Larson, J.; and Wild, S. M. Optimization Methods and Software, 28(5): 993–1011. 2013.
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Computational Nuclear Quantum Many-Body Problem: The UNEDF Project. Bogner, S.; Bulgac, A.; Carlson, J.; Enge, J.; Fann, G.; R.J.~Furnstahl; Gandolfi, S.; Hagen, G.; M.~Horoi; C.~Johnson; Kortelainen, M.; Lusk, E.; Maris, P.; Nam, H.; Navratil, P.; Nazarewicz, W.; Ng, E.; Nobre, G.; Ormand, E.; Papenbrock, T.; Pei, J.; Pieper, S. C.; Quaglioni, S.; Roche, K.; Sarich, J.; N.~Schunck; Sosonkina, M.; J.~Terasaki; Thompson, I.; J.P.~Vary; and Wild, S. Computer Physics Communications, 184(10): 2235–2250. 2013.
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Development of a Fast, Robust Numerical Tool for the Design, Optimization, and Control of IC Engines. Aithal, S. M.; and Wild, S. M. In Proceedings of the SAE 11th International Conference on Engines & Vehicles, 2013. SAE SAE Technical Paper 2013-24-0141
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Optimized Chiral Nucleon-Nucleon Interaction at Next-to-Next-to-Leading Order. Ekström, A.; Baardsen, G.; Forssén, C.; Hagen, G.; Hjorth-Jensen, M.; Jansen, G. R.; Machleidt, R.; Nazarewicz, W.; Papenbrock, T.; Sarich, J.; and Wild, S. M. Physical Review Letters, 110(19): 192502. May 2013.
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Novel Process to Use Vehicle Simulations Directly as Inputs to the VOLPE Model. Moawad, A.; Halbach, S.; Pagerit, S.; Rousseau, A.; Balaprakash, P.; and Wild, S. M. Technical Report ANL/ESD-13/13, Argonne National Laboratory, 12 2013.
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Variable Selection and Sensitivity Analysis via Dynamic Trees with an Application to Computer Code Performance Tuning. Gramacy, R. B.; Taddy, M. A.; and Wild, S. M. Annals of Applied Statistics, 7(1): 51–80. 2013.
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Global Convergence of Radial Basis Function Trust-Region Algorithms for Derivative-Free Optimization. Wild, S. M.; and Shoemaker, C. A. SIAM Review, 55(2): 349–371. 2013.
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  2012 (10)
SPAPT: Search Problems in Automatic Performance Tuning. Balaprakash, P.; Wild, S. M.; and Norris, B. Procedia Computer Science, 9: 1959–1968. 2012. In Proceedings of the International Conference on Computational Science, ICCS 2012
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Quality Input for Microscopic Fission Theory. Nazarewicz, W.; Schunck, N.; and Wild, S. M. Stockpile Stewardship Quarterly, 2(1). 2012.
Quality Input for Microscopic Fission Theory [link]Paper   bibtex
Estimating Derivatives of Noisy Simulations. Moré, J. J.; and Wild, S. M. ACM Transactions on Mathematical Software, 38(3): 19:1–19:21. 2012.
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Occupation Number-based Energy Functional for Nuclear Masses. Bertolli, M.; Papenbrock, T.; and Wild, S. M. Physical Review C, 85(1): 014322. 2012.
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Report on the Workshop on Extreme-Scale Solvers: Transition to Future Architectures. Ang, J.; Evans, K.; Geist, A.; Heroux, M.; Hovland, P.; Marques, O.; Curfman McInnes, L.; Ng, E.; and Wild, S. M. Technical Report U.S.\ Department of Energy, ASCR, April 2012.
Report on the Workshop on Extreme-Scale Solvers: Transition to Future Architectures [pdf]Paper   bibtex
The TAO Linearly Constrained Augmented Lagrangian Method for PDE-Constrained Optimization. Gawlik, E.; Munson, T.; Sarich, J.; and Wild, S. M. Technical Report ANL/MCS-P2003-0112, Argonne National Laboratory, Mathematics and Computer Science Division, January 2012.
The TAO Linearly Constrained Augmented Lagrangian Method for PDE-Constrained Optimization [pdf]Paper   bibtex
Benefits of Deeper Analysis in Simulation-based Groundwater Optimization Problems. Kannan, A.; and Wild, S. M. In Proceedings of the XIX International Conference on Computational Methods in Water Resources (CMWR 2012), June 2012.
Benefits of Deeper Analysis in Simulation-based Groundwater Optimization Problems [pdf]2   Benefits of Deeper Analysis in Simulation-based Groundwater Optimization Problems [pdf]Paper   bibtex
TAO 2.0 Users Manual. Munson, T.; Sarich, J.; Wild, S. M.; Benson, S.; and Curfman McInnes, L. Technical Report ANL/MCS-TM-322, Argonne National Laboratory, 2012.
TAO 2.0 Users Manual [pdf]Paper   bibtex
Nuclear Energy Density Optimization: Large Deformations. Kortelainen, M.; McDonnell, J.; Nazarewicz, W.; Reinhard, P.; Sarich, J.; Schunck, N.; Stoitsov, M.; and Wild, S. Physical Review C, 85: 024304. 2012.
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UNEDF: Advanced Scientific Computing Collaboration Transforms the Low-Energy Nuclear Many-Body Problem. Nam, H.; Stoitsov, M.; Nazarewicz, W.; Bulgac, A.; Hagen, G.; Kortelainen, M.; Maris, P.; Pei, J. C.; Roche, K. J.; Schunck, N.; Thompson, I.; Vary, J. P.; and Wild, S. M. Journal of Physics: Conference Series, 402: 012033. 2012.
UNEDF: Advanced Scientific Computing Collaboration Transforms the Low-Energy Nuclear Many-Body Problem [link]2   doi   bibtex
  2011 (7)
Advancing Nuclear Physics Through TOPS Solvers and Tools. Ng, E.; Sarich, J.; Munson, T.; Wild, S.; Aktulga, H.; Yang, C.; Maris, P.; Vary, J.; Kortelainen, M.; Nazarewicz, W.; Papenbrock, T.; Schunck, N.; Stoitsov, M.; and Bertolli, M. In Proceedings of SciDAC 2011, July 2011.
Advancing Nuclear Physics Through TOPS Solvers and Tools [pdf]Paper   bibtex
Obtaining Quadratic Models of Noisy Functions. Kannan, A.; and Wild, S. M. Technical Report ANL/MCS-P1975-1111, Argonne National Laboratory, Mathematics and Computer Science Division, November 2011.
Obtaining Quadratic Models of Noisy Functions [pdf]Paper   bibtex
Can Search Algorithms Save Large-Scale Automatic Performance Tuning?. Balaprakash, P.; Wild, S. M.; and Hovland, P. D. Procedia Computer Science (ICCS 2011), 4: 2136–2145. 2011.
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Advanced Scientific Computing Transforms the Low-Energy Nuclear Many-Body Problem. Stoitsov, M.; Nam, H.; Nazarewicz, W.; Bulgac, A.; Hagen, G.; Kortelainen, M.; Pei, J.; Roche, K.; Schunck, N.; Thompson, I.; Vary, J.; and Wild, S. In Proceedings of SciDAC 2011, July 2011.
Advanced Scientific Computing Transforms the Low-Energy Nuclear Many-Body Problem [pdf]Paper   bibtex
Computing Heavy Elements. Schunck, N.; Baran, A.; Kortelainen, M.; McDonnell, J.; Moré, J.; Nazarewicz, W.; Pei, J.; Sarich, J.; Sheikh, J.; Staszczak, A.; Stoitsov, M.; and Wild, S. In Proceedings of SciDAC 2011, July 2011.
Computing Heavy Elements [pdf]Paper   bibtex
Estimating Computational Noise. Moré, J. J.; and Wild, S. M. SIAM Journal on Scientific Computing, 33(3): 1292–1314. 2011.
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Global Convergence of Radial Basis Function Trust Region Derivative-Free Algorithms. Wild, S. M.; and Shoemaker, C. A. SIAM Journal on Optimization, 21(3): 761–781. 2011.
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  2010 (1)
Nuclear Energy Density Optimization. Kortelainen, M.; Lesinski, T.; Moré, J.; Nazarewicz, W.; Sarich, J.; Schunck, N.; Stoitsov, M.; and Wild, S. Physical Review C, 82(2): 024313. 2010.
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  2009 (2)
Towards The Universal Nuclear Energy Density Functional. Stoitsov, M.; Moré, J.; Nazarewicz, W.; Pei, J.; Sarich, J.; Schunck, N.; Staszczak, A.; and Wild, S. Journal of Physics: Conference Series, 180: 012082. 2009.
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Benchmarking Derivative-Free Optimization Algorithms. Moré, J. J.; and Wild, S. M. SIAM Journal on Optimization, 20(1): 172–191. 2009.
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  2008 (4)
MNH: A Derivative-Free Optimization Algorithm Using Minimal Norm Hessians. Wild, S. M. In Tenth Copper Mountain Conference on Iterative Methods, April 2008. Available at r̆lhttp://grandmaster.colorado.edu/~copper/2008/SCWinners/Wild.pdf
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Derivative-Free Optimization Algorithms for Computationally Expensive Functions. Wild, S. M. Ph.D. Thesis, Cornell University, 2008.
Derivative-Free Optimization Algorithms for Computationally Expensive Functions [link]Paper   bibtex
Bayesian Calibration of Computationally Expensive Models Using Optimization and Radial Basis Function Approximation. Bliznyuk, N.; Ruppert, D.; Shoemaker, C. A.; Regis, R. G.; Wild, S. M.; and Mugunthan, P. Journal of Computational and Graphical Statistics, 17(2): 270–294. 2008.
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ORBIT: Optimization by Radial Basis Function Interpolation in Trust-Regions. Wild, S. M.; Regis, R. G.; and Shoemaker, C. A. SIAM Journal on Scientific Computing, 30(6): 3197–3219. 2008.
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  2007 (1)
Maximizing Influence in a Competitive Social Network: A Follower's Perspective. Carnes, T.; Nagarajan, C.; Wild, S. M.; and van Zuylen, A. In ICEC '07: Proceedings of the Ninth International Conference on Electronic Commerce, pages 351–360, New York, 2007. ACM
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  2004 (1)
Improving Non-Negative Matrix Factorizations Through Structured Initialization. Wild, S. M.; Curry, J. H.; and Dougherty, A. Pattern Recognition, 37(11): 2217–2232. 2004.
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  2003 (2)
Seeding Non-Negative Matrix Factorizations with the Spherical K-Means Clustering. Wild, S. M. Master's Thesis, University of Colorado, 2003.
Seeding Non-Negative Matrix Factorizations with the Spherical K-Means Clustering [link]Paper   bibtex
Motivating Non-Negative Matrix Factorizations. Wild, S. M.; Curry, J. H.; and Dougherty, A. In Proceedings of the Eighth SIAM Conference on Applied Linear Algebra, July 2003.
Motivating Non-Negative Matrix Factorizations [pdf]Paper   bibtex
  2002 (1)
Probabilistically Optimized Airline Overbooking Strategies, or ``Anyone Willing to Take a Later Flight?!". Leder, K. Z.; Spagnolie, S. E.; and Wild, S. M. Journal of Undergraduate Mathematics and Its Applications, 23(3): 317–338. 2002.
Probabilistically Optimized Airline Overbooking Strategies, or ``Anyone Willing to Take a Later Flight?!" [link]Paper   bibtex