DERIVATIVE-FREE OPTIMIZATION Algorithms, software and ...

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FRACTION OF PROBLEMS IMPROVED: NONCONVEX NONSMOOTH 36
FRACTION OF PROBLEMS SOLVED: MULTISTART STRATEGY 1 to 2 variables 3 to 9 variables 10 to 30 variables 31 to 300 variables 37
Page 1 and 2: DERIVATIVE-FREE OPTIMIZATION Algori
Page 3 and 4: MODEL CALIBRATION (Maguthan and Sho
Page 5 and 6: TIMELINE OF INNOVATION 5
Page 7 and 8: DERIVATIVE-FREE OPTIMIZATION ALGORI
Page 9 and 10: PATTERN SEARCH ALGORITHMS 9
Page 11 and 12: ALGORITHMIC COMPONENTS • Random e
Page 13 and 14: SOLVERS CONSIDERED 13
Page 15 and 16: SEARCH PROGRESS FOR camel6—Contin
Page 17 and 18: TEST PROBLEM CHARACTERISTICS Over 5
Page 19 and 20: QUESTIONS ADDRESSED • What is the
Page 21 and 22: FRACTION OF PROBLEMS SOLVED: CONVEX
Page 23 and 24: FRACTION OF PROBLEMS SOLVED: NONCON
Page 25 and 26: FRACTION OF PROBLEMS SOLVER WAS BES
Page 27 and 28: FRACTION OF PROBLEMS SOLVER WAS BES
Page 29 and 30: FRACTION OF PROBLEMS SOLVED: 3 TO 9
Page 31 and 32: FRACTION OF PROBLEMS SOLVED: 31 TO
Page 33 and 34: FRACTION OF PROBLEMS IMPROVED: CONV
Page 35: FRACTION OF PROBLEMS IMPROVED: NONC
Page 39 and 40: MINIMUM SET OF SOLVERS CONVEX NONSM
Page 41 and 42: MINIMUM SET OF SOLVERS NONCONVEX NO
Page 43 and 44: REFINEMENT ABILITY • Solvers were
Page 45 and 46: MODEL-AND-SEARCH LOCAL ALGORITHM Π
Page 47 and 48: PROTEIN-LIGAND DOCKING • Identify
Page 49: CONCLUSIONS • MCS, LGO, and NEWOA

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