91 research outputs found
A local branching heuristic for MINLPs
Local branching is an improvement heuristic, developed within the context of
branch-and-bound algorithms for MILPs, which has proved to be very effective in
practice. For the binary case, it is based on defining a neighbourhood of the
current incumbent solution by allowing only a few binary variables to flip
their value, through the addition of a local branching constraint. The
neighbourhood is then explored with a branch-and-bound solver. We propose a
local branching scheme for (nonconvex) MINLPs which is based on iteratively
solving MILPs and NLPs. Preliminary computational experiments show that this
approach is able to improve the incumbent solution on the majority of the test
instances, requiring only a short CPU time. Moreover, we provide algorithmic
ideas for a primal heuristic whose purpose is to find a first feasible
solution, based on the same scheme
Overview on mixed integer nonlinear programming problems
Many optimization problems involve integer and continuous variables that can be modeled as mixed integer nonlinear programming (MINLP) problems. This has led to a wide range of applications, in particular in some engineering areas. Here, we provide a brief overview on MINLP, and present a simple idea for a future nonconvex MINLP solution technique.Fundação para a Ciência e a Tecnologia (FCT
Proposed shunt rounding technique for large-scale security constrained loss minimization
The official published version can be obtained from the link below - Copyright @ 2010 IEEE.Optimal reactive power flow applications often model large numbers of discrete shunt devices as continuous variables, which are rounded to their nearest discrete value at the final iteration. This can degrade optimality. This paper presents novel methods based on probabilistic and adaptive threshold approaches that can extend existing security constrained optimal reactive power flow methods to effectively solve large-scale network problems involving discrete shunt devices. Loss reduction solutions from the proposed techniques were compared to solutions from the mixed integer nonlinear mathematical programming algorithm (MINLP) using modified IEEE standard networks up to 118 buses. The proposed techniques were also applied to practical large-scale network models of Great Britain. The results show that the proposed techniques can achieve improved loss minimization solutions when compared to the standard rounding method.This work was supported in part by the National Grid and in part by the EPSRC. Paper no. TPWRS-00653-2009
A feasibility-based algorithm for Computer Aided Molecular and Process Design of solvent-based separation systems
Computer-aided molecular and product design (CAMPD) can in principle be used to find simultaneously the optimal conditions in separation processes and the structure of the optimal solvents. In many cases, however, the solution of CAMPD problems is challenging. In this paper, we propose a solution approach for the CAMPD of solvent-based separation systems in which implicit constraints on phase behaviour in process models are used to test the feasibility of the process and solvent domains. The tests not only eliminate infeasible molecules from the search space but also infeasible combinations of solvent molecules and process conditions. The tests also provide bounds for the optimization of the process model (primal problem) for each solvent, facilitating numerical solution. This is demonstrated on a prototypical natural gas purification process
Extended Formulations in Mixed-integer Convex Programming
We present a unifying framework for generating extended formulations for the
polyhedral outer approximations used in algorithms for mixed-integer convex
programming (MICP). Extended formulations lead to fewer iterations of outer
approximation algorithms and generally faster solution times. First, we observe
that all MICP instances from the MINLPLIB2 benchmark library are conic
representable with standard symmetric and nonsymmetric cones. Conic
reformulations are shown to be effective extended formulations themselves
because they encode separability structure. For mixed-integer
conic-representable problems, we provide the first outer approximation
algorithm with finite-time convergence guarantees, opening a path for the use
of conic solvers for continuous relaxations. We then connect the popular
modeling framework of disciplined convex programming (DCP) to the existence of
extended formulations independent of conic representability. We present
evidence that our approach can yield significant gains in practice, with the
solution of a number of open instances from the MINLPLIB2 benchmark library.Comment: To be presented at IPCO 201
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