707 research outputs found
An algebraic approach to Integer Portfolio problems
Integer variables allow the treatment of some portfolio optimization problems in a more realistic way and introduce the possibility of adding some natural features to the model.
We propose an algebraic approach to maximize the expected return under a given admissible level of risk measured by the covariance matrix. To reach an optimal portfolio it is an essential ingredient the computation of different test sets (via Gr\"obner basis) of linear subproblems that are used in a dual search strategy.Universidad de Sevilla P06-FQM-01366Junta de Andalucía (Plan Andaluz de Investigación) FQM-333Ministerio de Ciencia e Innovación (España) MTM2007-64509Instituto de Matemáticas de la Universidad de Sevilla MTM2007-67433-C02-0
Sticky Brownian Rounding and its Applications to Constraint Satisfaction Problems
Semidefinite programming is a powerful tool in the design and analysis of
approximation algorithms for combinatorial optimization problems. In
particular, the random hyperplane rounding method of Goemans and Williamson has
been extensively studied for more than two decades, resulting in various
extensions to the original technique and beautiful algorithms for a wide range
of applications. Despite the fact that this approach yields tight approximation
guarantees for some problems, e.g., Max-Cut, for many others, e.g., Max-SAT and
Max-DiCut, the tight approximation ratio is still unknown. One of the main
reasons for this is the fact that very few techniques for rounding semidefinite
relaxations are known.
In this work, we present a new general and simple method for rounding
semi-definite programs, based on Brownian motion. Our approach is inspired by
recent results in algorithmic discrepancy theory. We develop and present tools
for analyzing our new rounding algorithms, utilizing mathematical machinery
from the theory of Brownian motion, complex analysis, and partial differential
equations. Focusing on constraint satisfaction problems, we apply our method to
several classical problems, including Max-Cut, Max-2SAT, and MaxDiCut, and
derive new algorithms that are competitive with the best known results. To
illustrate the versatility and general applicability of our approach, we give
new approximation algorithms for the Max-Cut problem with side constraints that
crucially utilizes measure concentration results for the Sticky Brownian
Motion, a feature missing from hyperplane rounding and its generalization
An outer approximation bi-level framework for mixed categorical structural optimization problems
In this paper, mixed categorical structural optimization problems are
investigated. The aim is to minimize the weight of a truss structure with
respect to cross-section areas, materials and cross-section type. The proposed
methodology consists of using a bi-level decomposition involving two problems:
master and slave. The master problem is formulated as a mixed integer linear
problem where the linear constraints are incrementally augmented using outer
approximations of the slave problem solution. The slave problem addresses the
continuous variables of the optimization problem. The proposed methodology is
tested on three different structural optimization test cases with increasing
complexity. The comparison to state-of-the-art algorithms emphasizes the
efficiency of the proposed methodology in terms of the optimum quality,
computation cost, as well as its scalability with respect to the problem
dimension. A challenging 120-bar dome truss optimization problem with 90
categorical choices per bar is also tested. The obtained results showed that
our method is able to solve efficiently large scale mixed categorical
structural optimization problems.Comment: Accepted for publication in Structural and Multidisciplinary
Optimization, to appear 202
Efficient Semidefinite Branch-and-Cut for MAP-MRF Inference
We propose a Branch-and-Cut (B&C) method for solving general MAP-MRF
inference problems. The core of our method is a very efficient bounding
procedure, which combines scalable semidefinite programming (SDP) and a
cutting-plane method for seeking violated constraints. In order to further
speed up the computation, several strategies have been exploited, including
model reduction, warm start and removal of inactive constraints.
We analyze the performance of the proposed method under different settings,
and demonstrate that our method either outperforms or performs on par with
state-of-the-art approaches. Especially when the connectivities are dense or
when the relative magnitudes of the unary costs are low, we achieve the best
reported results. Experiments show that the proposed algorithm achieves better
approximation than the state-of-the-art methods within a variety of time
budgets on challenging non-submodular MAP-MRF inference problems.Comment: 21 page
A Parallel Branch and Bound Algorithm for Integer Linear Programming Models
A parallel branch and bound algorithm is developed for use with MIMD computers to study the efficiency of parallel processors on general integer linear programming problems. The Haldi and IBM test problems and a System Design model are used in the implementation of the algorithm. Initially the algorithm solves the Haldi and IBM test problems on a single processor computer which simulates a multiple processor computer. The algorithm is then implemented on the Denelcor HEP multiprocessor using two of the IBM problems to compare the results of the simulation to the results using an MIMD computer. Finally the algorithm is implemented on the HEP using the System Design model to show a case in which the number of pivots decreases as the number of processes are increased from seven to the process limit of sixteen.
In general, it is shown that super linear efficiency can be achieved using multiple processors
On the integration of Dantzig-Wolfe and Fenchel decompositions via directional normalizations
The strengthening of linear relaxations and bounds of mixed integer linear
programs has been an active research topic for decades. Enumeration-based
methods for integer programming like linear programming-based branch-and-bound
exploit strong dual bounds to fathom unpromising regions of the feasible space.
In this paper, we consider the strengthening of linear programs via a composite
of Dantzig-Wolfe and Fenchel decompositions. We provide geometric
interpretations of these two classical methods. Motivated by these geometric
interpretations, we introduce a novel approach for solving Fenchel sub-problems
and introduce a novel decomposition combining Dantzig-Wolfe and Fenchel
decompositions in an original manner. We carry out an extensive computational
campaign assessing the performance of the novel decomposition on the
unsplittable flow problem. Very promising results are obtained when the new
approach is compared to classical decomposition methods
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