@inproceedings{2016shahunpasWSC,
	Year = {2016},
	Author = {S. Shashaani and S. R. Hunter and R. Pasupathy},
	Title = {{ASTRO-DF}: Adaptive sampling trust-region optimization algorithms, heuristics, and numerical experience},
	Booktitle = {Proceedings of the 2016 Winter Simulation Conference},
	Editor = {T. M. K. Roeder and P. I. Frazier and R. Szechtman and E. Zhou},
	Publisher = {Institute of Electrical and Electronics Engineers, Inc.},
        Address = {Piscataway, NJ},
        Pages = {554--565},
        doi = {10.1109/WSC.2016.7822121},
        url_Paper = {http://www.informs-sim.org/wsc16papers/050.pdf},
        abstract = {ASTRO-DF is a class of adaptive sampling algorithms for solving simulation optimization problems in which only estimates of the objective function are available by executing a Monte Carlo simulation. ASTRO-DF algorithms are iterative trust-region algorithms, where a local model is repeatedly constructed and optimized as iterates evolve through the search space. The ASTRO-DF class of algorithms is derivative-free in the sense that it does not rely on direct observations of the function derivatives. A salient feature of ASTRO-DF is the incorporation of adaptive sampling and replication to keep the model error and the trust-region radius in lock-step, to ensure efficiency. ASTRO-DF has been demonstrated to generate iterates that globally converge to a first-order critical point with probability one. In this paper, we describe and list ASTRO-DF, and discuss key heuristics that ensure good finite-time performance. We report our numerical experience with ASTRO-DF on test problems in low to moderate dimensions.},
        keywords = {simulation optimization > single-objective > continuous},
        bibbase_note = {<span style="color: green">2016 Winter Simulation Conference I-Sim Best Student Paper Award.</span>}}

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ASTRO-DF algorithms are iterative trust-region algorithms, where a local model is repeatedly constructed and optimized as iterates evolve through the search space. The ASTRO-DF class of algorithms is derivative-free in the sense that it does not rely on direct observations of the function derivatives. A salient feature of ASTRO-DF is the incorporation of adaptive sampling and replication to keep the model error and the trust-region radius in lock-step, to ensure efficiency. ASTRO-DF has been demonstrated to generate iterates that globally converge to a first-order critical point with probability one. In this paper, we describe and list ASTRO-DF, and discuss key heuristics that ensure good finite-time performance. We report our numerical experience with ASTRO-DF on test problems in low to moderate dimensions.","keywords":"simulation optimization > single-objective > continuous","bibbase_note":"<span style=\"color: green\">2016 Winter Simulation Conference I-Sim Best Student Paper Award.</span>","bibtex":"@inproceedings{2016shahunpasWSC,\n\tYear = {2016},\n\tAuthor = {S. Shashaani and S. R. Hunter and R. Pasupathy},\n\tTitle = {{ASTRO-DF}: Adaptive sampling trust-region optimization algorithms, heuristics, and numerical experience},\n\tBooktitle = {Proceedings of the 2016 Winter Simulation Conference},\n\tEditor = {T. M. K. Roeder and P. I. Frazier and R. Szechtman and E. Zhou},\n\tPublisher = {Institute of Electrical and Electronics Engineers, Inc.},\n Address = {Piscataway, NJ},\n Pages = {554--565},\n doi = {10.1109/WSC.2016.7822121},\n url_Paper = {http://www.informs-sim.org/wsc16papers/050.pdf},\n abstract = {ASTRO-DF is a class of adaptive sampling algorithms for solving simulation optimization problems in which only estimates of the objective function are available by executing a Monte Carlo simulation. ASTRO-DF algorithms are iterative trust-region algorithms, where a local model is repeatedly constructed and optimized as iterates evolve through the search space. The ASTRO-DF class of algorithms is derivative-free in the sense that it does not rely on direct observations of the function derivatives. A salient feature of ASTRO-DF is the incorporation of adaptive sampling and replication to keep the model error and the trust-region radius in lock-step, to ensure efficiency. ASTRO-DF has been demonstrated to generate iterates that globally converge to a first-order critical point with probability one. In this paper, we describe and list ASTRO-DF, and discuss key heuristics that ensure good finite-time performance. We report our numerical experience with ASTRO-DF on test problems in low to moderate dimensions.},\n keywords = {simulation optimization > single-objective > continuous},\n bibbase_note = {<span style=\"color: green\">2016 Winter Simulation Conference I-Sim Best Student Paper Award.</span>}}\n\n","author_short":["Shashaani, S.","Hunter, S. R.","Pasupathy, R."],"editor_short":["Roeder, T. M. K.","Frazier, P. 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