198 research outputs found
SDP-based bounds for the Quadratic Cycle Cover Problem via cutting plane augmented Lagrangian methods and reinforcement learning
We study the Quadratic Cycle Cover Problem (QCCP), which aims to find a
node-disjoint cycle cover in a directed graph with minimum interaction cost
between successive arcs. We derive several semidefinite programming (SDP)
relaxations and use facial reduction to make these strictly feasible. We
investigate a nontrivial relationship between the transformation matrix used in
the reduction and the structure of the graph, which is exploited in an
efficient algorithm that constructs this matrix for any instance of the
problem. To solve our relaxations, we propose an algorithm that incorporates an
augmented Lagrangian method into a cutting plane framework by utilizing
Dykstra's projection algorithm. Our algorithm is suitable for solving SDP
relaxations with a large number of cutting planes. Computational results show
that our SDP bounds and our efficient cutting plane algorithm outperform other
QCCP bounding approaches from the literature. Finally, we provide several
SDP-based upper bounding techniques, among which a sequential Q-learning method
that exploits a solution of our SDP relaxation within a reinforcement learning
environment
Minimizing Nonconvex Quadratic Functions Subject to Bound Constraints
We present an active-set algorithm for finding a local minimizer to a nonconvex
bound-constrained quadratic problem. Our algorithm extends the ideas developed by Dost al and Sch oberl that is based on the linear conjugate gradient algorithm for (approximately) solving a linear system with a positive-de finite coefficient matrix.
This is achieved by making two key changes. First, we perform a line search along negative curvature directions when they are encountered in the linear conjugate gradient iteration. Second, we use Lanczos iterations to compute approximations to leftmost eigen-pairs, which is needed to promote convergence to points satisfying certain second-order optimality conditions. Preliminary numerical results show that our
method is e fficient and robust on nonconvex bound-constrained quadratic problems
Reformulation and decomposition of integer programs
In this survey we examine ways to reformulate integer and mixed integer programs. Typically, but not exclusively, one reformulates so as to obtain stronger linear programming relaxations, and hence better bounds for use in a branch-and-bound based algorithm. First we cover in detail reformulations based on decomposition, such as Lagrangean relaxation, Dantzig-Wolfe column generation and the resulting branch-and-price algorithms. This is followed by an examination of Benders’ type algorithms based on projection. Finally we discuss in detail extended formulations involving additional variables that are based on problem structure. These can often be used to provide strengthened a priori formulations. Reformulations obtained by adding cutting planes in the original variables are not treated here.Integer program, Lagrangean relaxation, column generation, branch-and-price, extended formulation, Benders' algorithm
Integrality and cutting planes in semidefinite programming approaches for combinatorial optimization
Many real-life decision problems are discrete in nature. To solve such problems as mathematical optimization problems, integrality constraints are commonly incorporated in the model to reflect the choice of finitely many alternatives. At the same time, it is known that semidefinite programming is very suitable for obtaining strong relaxations of combinatorial optimization problems. In this dissertation, we study the interplay between semidefinite programming and integrality, where a special focus is put on the use of cutting-plane methods. Although the notions of integrality and cutting planes are well-studied in linear programming, integer semidefinite programs (ISDPs) are considered only recently. We show that manycombinatorial optimization problems can be modeled as ISDPs. Several theoretical concepts, such as the Chvátal-Gomory closure, total dual integrality and integer Lagrangian duality, are studied for the case of integer semidefinite programming. On the practical side, we introduce an improved branch-and-cut approach for ISDPs and a cutting-plane augmented Lagrangian method for solving semidefinite programs with a large number of cutting planes. Throughout the thesis, we apply our results to a wide range of combinatorial optimization problems, among which the quadratic cycle cover problem, the quadratic traveling salesman problem and the graph partition problem. Our approaches lead to novel, strong and efficient solution strategies for these problems, with the potential to be extended to other problem classes
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