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Title: Asynchronously parallel optimization solver for finding multiple minima

Journal Article · · Mathematical Programming Computation
 [1];  [1]
  1. Argonne National Lab. (ANL), Lemont, IL (United States)
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This paper proposes and analyzes an asynchronously parallel optimization algorithm for finding multiple, high-quality minima of nonlinear optimization problems. Our multistart algorithm considers all previously evaluated points when determining where to start or continue a local optimization run. Theoretical results show that, under certain assumptions, the algorithm almost surely starts a finite number of local optimization runs and identifies, or has a single local optimization run converging to, every minimum. The algorithm is applicable to general optimization settings, but our numerical results focus on the case when derivatives are unavailable. In numerical tests, a PYTHON implementation of the algorithm is shown to yield good approximations of many minima (including a global minimum), and this ability scales well with additional resources. Our implementation’s time to solution is shown also to scale well even when the time to evaluate the function evaluation is highly variable.

Research Organization:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Basic Energy Sciences (BES)
Grant/Contract Number:
AC02-06CH11357
OSTI ID:
1466333
Journal Information:
Mathematical Programming Computation, Vol. 10, Issue 3; ISSN 1867-2949
Publisher:
SpringerCopyright Statement
Country of Publication:
United States
Language:
English

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Figures / Tables (15)

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97 MATHEMATICS AND COMPUTING
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