An efficient multiple shooting based reduced SQP strategy for large-scale dynamic process optimization

Leineweber, Daniel B.; Schäfer, Andreas; Bock, Hans Georg; Schlöder, Johannes P.

doi:10.1016/s0098-1354(02)00195-3

Cited by 168 publications

(127 citation statements)

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“…From this discretization and the (algorithm specific) parameterization of the differential states results a highly structured NLP that is usually solved by either an interior point or an active set based algorithm. For details on an efficient implementation and further references see, e.g., [24,4].…”

Section: Direct Approach To Optimal Controlmentioning

confidence: 99%

Reformulations and algorithms for the optimization of switching decisions in nonlinear optimal control

Säger

2009

Journal of Process Control

120

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Section: Direct Approach To Optimal Controlmentioning

confidence: 99%

Reformulations and algorithms for the optimization of switching decisions in nonlinear optimal control

Säger

2009

Journal of Process Control

120

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“…Early examples of such work include the development of parallel solvers for large scale linear and nonlinear systems in chemical process simulations (Paloschi, 1997(Paloschi, , 1998Scott, 2001Scott, , 2001a, dynamic simulations of chemical processes (AbdelJabbar et al, 1999;Leineweber et al, 2003), and an assessment of parallel computing paradigms (Brochard, 1998). Examples of papers on applied optimization include: the solution of process engineering problems that lead to non-convex mixed integer nonlinear optimization problems Pantelides, 1997, 1999;Schilling and Pantelides, 1999); the use of a parallel implementation of the discrete element method (Bertrand et al, 2004), applying the message passing interface (MPI) to shorten the computer time needed to simulate the packing of spherical particles as a model of the consolidation of paper coating structures; the development of a framework (Siirola et al, 2003) to integrate several optimization algorithms into a cooperative method that outperforms its individual components in terms of locating the global minimum of complicated functions.…”

Section: Introductionmentioning

confidence: 99%

Fitting equation of state parameters in parallel computers

Castier

Checoni

Zuber

2014

Braz. J. Chem. Eng.

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-This work compares two strategies to fit parameters of equations of state in parallel computers, emphasizing solutions that require few changes to existing sequential programs. One strategy uses the conventional Nelder-Mead algorithm coupled with parallel objective function evaluation (SSPO). The other strategy uses a parallel Nelder-Mead algorithm coupled with sequential objective function evaluation (PSSO). The PSSO strategy, which executes parallel one-dimensional searches during each iteration, is simpler to implement and converged to parameter sets with objective functions smaller than those obtained by the SSPO strategy. The SSPO strategy produced speedups consistent with the number of processes used and is more suitable when many processors are available. Both strategies are potentially useful and choosing between them is a matter of convenience, depending on the problem at hand. With parallel computers increasingly available, the easy implementation and convenience of these two strategies should appeal to developers and users of thermodynamic models.

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“…For our target problems the operators/matrices arising in the multiple shooting reformulation of the problem cannot be computed explicitly; only operator/matrix-vector products can be formed. Therefore existing multiple shooting implementations for systems of ODEs or 'small' PDEs (see, e.g., [13,34,41,45,73,142,143,176,202]), which rely on matrix factorizations are not applicable. Our approach is matrix free.…”

Section: Introductionmentioning

confidence: 99%