Improving the performance of DICOPT in convex MINLP problems using a feasibility pump

Bernal, David E.; Vigerske, Stefan; Trespalacios, Francisco; Grossmann, Ignacio E.

doi:10.1080/10556788.2019.1641498

Cited by 22 publications

(20 citation statements)

References 17 publications

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“…Finding a good feasible solution to an MINLP problem can improve the performance of MINLP solvers, as shown by the numerical results in Berthold (2014a) and Bernal et al (2017). Having a good feasible solution can, e.g., reduce the size of the search tree in BB-based solvers and provide a tight upper bound.…”

Section: Primal Heuristicsmentioning

confidence: 98%

“…Both methods are intended as heuristics for nonconvex MINLP problems, although if the equality constraints relax as convex inequalities the methods become rigorous. A feasibility pump algorithm is implemented as a primal heuristic to improve the solver's performance (Bernal et al 2017). DICOPT can use any available MILP and NLP subsolvers available in GAMS.…”

Section: Aoamentioning

confidence: 99%

“…This toolbox implements the ECP, GBD, and OA algorithms, together with primal heuristics. It relies on Pyomo to handle the resulting MILP and NLP subproblems, allowing any of the solvers compatible with Pyomo to be used with MindtPy (Bernal et al 2018). The toolbox is available in the following repository www.githu b.com/berna lde/pyomo /tree/mindt py.…”

Section: Other Minlp Solversmentioning

confidence: 99%

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A review and comparison of solvers for convex MINLP

et al. 2018

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In this paper, we present a review of deterministic software for solving convex MINLP problems as well as a comprehensive comparison of a large selection of commonly available solvers. As a test set, we have used all MINLP instances classified as convex in the problem library MINLPLib, resulting in a test set of 335 convex MINLP instances. A summary of the most common methods for solving convex MINLP problems is given to better highlight the differences between the solvers. To show how the solvers perform on problems with different properties, we have divided the test set into subsets based on the continuous relaxation gap, the degree of nonlinearity, and the relative number of discrete variables. The results also provide guidelines on how well suited a specific solver or method is for particular types of MINLP problems.

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Section: Primal Heuristicsmentioning

confidence: 98%

Section: Aoamentioning

confidence: 99%

Section: Other Minlp Solversmentioning

confidence: 99%

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A review and comparison of solvers for convex MINLP

et al. 2018

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“…The OA-based DICOPT solver uses the methods of equality relaxation and augmented penalty to improve its performance for nonconvex problems [28,33,72]. A good summary of the additional nonconvex strategies in DICOPT is given in [3].…”

Section: Polyhedral Approximationmentioning

confidence: 99%

“…In the future, additional methods such as the center-cut algorithm [34], rounding heuristics [4] or feasibility pumps [1,3,18] are also planned. The NLP relaxations are solved either by interfacing with the NLP solvers in GAMS (if available) or IPOPT [73].…”

Section: Primal Heuristicsmentioning

confidence: 99%

Polyhedral approximation strategies for nonconvex mixed-integer nonlinear programming in SHOT

Lundell

Kronqvist

2021

J Glob Optim

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Different versions of polyhedral outer approximation are used by many algorithms for mixed-integer nonlinear programming (MINLP). While it has been demonstrated that such methods work well for convex MINLP, extending them to solve nonconvex problems has traditionally been challenging. The Supporting Hyperplane Optimization Toolkit (SHOT) is a solver based on polyhedral approximations of the nonlinear feasible set of MINLP problems. SHOT is an open source COIN-OR project, and is currently one of the most efficient global solvers for convex MINLP. In this paper, we discuss some extensions to SHOT that significantly extend its applicability to nonconvex problems. The functionality include utilizing convexity detection for selecting the nonlinearities to linearize, lifting reformulations for special classes of functions, feasibility relaxations for infeasible subproblems and adding objective cuts to force the search for better feasible solutions. This functionality is not unique to SHOT, but can be implemented in other similar methods as well. In addition to discussing the new nonconvex functionality of SHOT, an extensive benchmark of deterministic solvers for nonconvex MINLP is performed that provides a snapshot of the current state of nonconvex MINLP.

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Distributed manufacturing for electrified chemical processes in a microgrid

Ramanujam,

Constante‐Flores,

2023

AIChE Journal

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To alleviate the greenhouse gas emissions by the chemical industry, electrification has been proposed as a solution where electricity from renewable sources is used to power processes. The adoption of renewable energy is complicated by its spatial and temporal variations. To address this challenge, we investigate the potential of distributed manufacturing for electrified chemical processes installed in a microgrid. We propose a multiscale mixed‐integer linear programming model for locating modular electrified plants, renewable‐based generating units, and power lines in a microgrid that includes monthly transportation and hourly scheduling decisions. We propose a K‐means clustering‐based aggregation disaggregation matheuristic to solve the model efficiently. The model and algorithm are tested using a case study with 20 candidate locations in Western Texas. Additionally, we define a new concept, “the Value of the Multi‐scale Model,” to demonstrate the additional economic benefits of our model compared with a single‐scale model.

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Improving the performance of DICOPT in convex MINLP problems using a feasibility pump

Cited by 22 publications

References 17 publications

A review and comparison of solvers for convex MINLP

A review and comparison of solvers for convex MINLP

Polyhedral approximation strategies for nonconvex mixed-integer nonlinear programming in SHOT

Distributed manufacturing for electrified chemical processes in a microgrid

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