Improving an interior-point approach for large block-angular problems by hybrid preconditioners

The computational time required by interior-point methods is often dominated by the solution of linear systems of equations. An efficient specialized interior-point algorithm for primal block-angular problems has been used to solve these systems by combining Cholesky factorizations for the block constraints and a conjugate gradient based on a power series preconditioner for the linking constraints. In some problems this power series preconditioner resulted to be inefficient on the last interior-point iterations, when the systems became ill-conditioned. In this work this approach is combined with a splitting preconditioner based on LU factorization, which works well for the last interior-point iterations. Computational results are provided for three classes of problems: multicommodity flows (oriented and nonoriented), minimum-distance controlled tabular adjustment for statistical data protection, and the minimum congestion problem. The results show that, in most cases, the hybrid preconditioner improves the performance and robustness of the interior-point solver. In particular, for some block-angular problems the solution time is reduced by a factor of 10.     

Quadratic regularizations in an interior-point method for primal block-angular problems

One of the most efficient interior-point methods for some classes of primal block-angular problems solves the normal equations by a combination of Cholesky factorizations and preconditioned conjugate gradient for, respectively, the block and linking constraints. Its efficiency depends on the spectral radius—in [0,1)— of a certain matrix in the definition of the preconditioner. Spectral radius close to 1 degrade the performance of the approach. The purpose of this work is twofold. First, to show that a separable quadratic regularization term in the objective reduces the spectral radius, significantly improving the overall performance in some classes of instances. Second, to consider a regularization term which decreases with the barrier function, thus with no need for an extra parameter. Computational experience with some primal block-angular problems confirms the efficiency of the regularized approach. In particular, for some difficult problems, the solution time is reduced by a factor of two to ten by the regularization term, outperforming state-of-the-art commercial solvers.     

An implementation of linear and nonlinear multicommodity network flows

This work presents a new code for solving the multicommodity network flow problem with a linear or nonlinear objective function considering additional linear side constraints that link arcs of the same or different commodities. For the multicommodity network flow problem through primal partitioning the code implements a specialization of Murtagh and Saunders' strategy of dividing the set of variables into basic, nonbasic and superbasic. Several tests are reported, using random problems obtained from different network generators and real problems arising from the fields of long and short-term hydrothermal scheduling of electricity generation and traffic assignment, with sizes of up to 150000 variables and 45 000 constraints The performance of the code developed is compared to that of alternative methodologies for solving the same problems: a general purpose linear and nonlinear constrained optimization code, a specialised linear multicommodity network flow code and a primal-dual interior point code.     

Solving L 1-CTA in 3D tables by an interior-point method for primal block-angular problems

The purpose of the field of statistical disclosure control is to avoid that confidential information could be derived from statistical data released by, mainly, national statistical agencies. Controlled tabular adjustment (CTA) is an emerging technique for the protection of statistical tabular data. Given a table with sensitive information, CTA looks for the closest safe table. In this work we focus on CTA for three-dimensional tables using the L ₁ norm for the distance between the original and protected tables. Three L ₁-CTA models are presented, giving rise to six different primal block-angular structures of the constraint matrices. The resulting linear programming problems are solved by a specialized interior-point algorithm for this constraints structure which solves the normal equations by a combination of Cholesky factorization and preconditioned conjugate gradients (PCG). In the past this algorithm was shown to be one of the most efficient approaches for some classes of block-angular problems. The effect of quadratic regularizations is also analyzed, showing that for three of the six primal block-angular structures the performance of PCG is guaranteed to improve. Computational results are reported for a set of large instances, which provide linear optimization problems of up to 50 million variables and 25 million constraints. The specialized interior-point algorithm is compared with the state-of-the-art barrier solver of the CPLEX 12.1 package, showing to be a more efficient choice for very large L ₁-CTA instances.     

An algorithm for hierarchical optimization of large-scale problems with nested structure

This paper describes a general concept and a particular optimization algorithm for solving a class of large-scale nonlinear programming problems with a nested block-angular structured system of linear constraints with coupling variables. A primal optimization algorithm is developed, which is based on the recursive application of the partitioning concept to the nested structure in combination with a feasible directions method. The special column by column application of this partitioning concept finally leads to a very clear and efficient algorithm for nested problems, which is called ‘successive partitioning method’. It is shown that the reduced-gradient method can be represented as a special application of the concept.     

Multicommodity Flow Problem with Variable Arcs Capacities

The problem of maximizing the sum of the flows of all commodities in a network where the capacities of some arcs can be increased by integer numbers within a fixed budget is solved in this paper. Benders' technique is used to decompose the problem. Then Rosen's primal partitioning and non-linear duality theory are used to solve the subproblems generated by the Benders' decomposition. An application of a multicommodity network to the defence problem is mentioned.     

Uniform preconditioners for a parameter dependent saddle point problem with application to generalized Stokes interface equations

We consider an abstract parameter dependent saddle-point problem and present a general framework for analyzing robust Schur complement preconditioners. The abstract analysis is applied to a generalized Stokes problem, which yields robustness of the Cahouet-Chabard preconditioner. Motivated by models for two-phase incompressible flows we consider a generalized Stokes interface problem. Application of the general theory results in a new Schur complement preconditioner for this class of problems. The robustness of this preconditioner with respect to several parameters is treated. Results of numerical experiments are given that illustrate robustness properties of the preconditioner.     

Lagrangean relaxation based heuristics for lot sizing with setup times

We consider a lot sizing problem with setup times where the objective is to minimize the total inventory carrying cost only. The demand is dynamic over time and there is a single resource of limited capacity. We show that the approaches implemented in the literature for more general versions of the problem do not perform well in this case. We examine the Lagrangean relaxation (LR) of demand constraints in a strong reformulation of the problem. We then design a primal heuristic to generate upper bounds and combine it with the LR problem within a subgradient optimization procedure. We also develop a simple branch and bound heuristic to solve the problem. Computational results on test problems taken from the literature show that our relaxation procedure produces consistently better solutions than the previously developed heuristics in the literature.     

Parallel Implementation of a Central Decomposition Method for Solving Large-Scale Planning Problems

We use a decomposition approach to solve three types of realistic problems: block-angular linear programs arising in energy planning, Markov decision problems arising in production planning and multicommodity network problems arising in capacity planning for survivable telecommunication networks. Decomposition is an algorithmic device that breaks down computations into several independent subproblems. It is thus ideally suited to parallel implementation. To achieve robustness and greater reliability in the performance of the decomposition algorithm, we use the Analytic Center Cutting Plane Method (ACCPM) to handle the master program. We run the algorithm on two different parallel computing platforms: a network of PC's running under Linux and a genuine parallel machine, the IBM SP2. The approach is well adapted for this coarse grain parallelism and the results display good speed-up's for the classes of problems we have treated.     

Bundle-based decomposition for large-scale convex optimization: Error estimate and application to block-angular linear programs

Robinson has proposed the bundle-based decomposition algorithm to solve a class of structured large-scale convex optimization problems. In this method, the original problem is transformed (by dualization) to an unconstrained nonsmooth concave optimization problem which is in turn solved by using a modified bundle method. In this paper, we give a posteriori error estimates on the approximate primal optimal solution and on the duality gap. We describe implementation and present computational experience with a special case of this class of problems, namely, block-angular linear programming problems. We observe that the method is efficient in obtaining the approximate optimal solution and compares favorably with MINOS and an advanced implementation of the Dantzig—Wolfe decomposition method.     

A capacity scaling heuristic for the multicommodity capacitated network design problem

In this paper, we propose a capacity scaling heuristic using a column generation and row generation technique to address the multicommodity capacitated network design problem. The capacity scaling heuristic is an approximate iterative solution method for capacitated network problems based on changing arc capacities, which depend on flow volumes on the arcs. By combining a column and row generation technique and a strong formulation including forcing constraints, this heuristic derives high quality results, and computational effort can be reduced considerably. The capacity scaling heuristic offers one of the best current results among approximate solution algorithms designed to address the multicommodity capacitated network design problem.     

A Primal Simplex Based Solution Procedure for the Rectilinear Distance Multifacility Location Problem

In this paper a primal simplex based solution procedure is developed for a multiproduct, multi-facility location problem using rectilinear distance measures. By overcoming the combinatorial nature of the problem which arises in the presence of degeneracy, this procedure is capable of handling large size problems. Computational results are also provided.     

Multicommodity flow models for spanning trees with hop constraints

In this paper we compare the linear programming relaxations of undirected and directed multicommodity flow formulations for the terminal layout problem with hop constraints. Hop constraints limit the number of hops (links) between the computer center and any terminal in the network. These constraints model delay constraints since a smaller number of hops decreases the maximum delay transmission time in the network. They also model reliability constraints because with a smaller number of hops there is a lower route loss probability. Hop constraints are easily modelled with the variables involved in multicommodity flow formulations. We give some empirical evidence showing that the linear programming relaxation of such formulations give sharp lower bounds for this hop constrained network design problem. On the other hand, these formulations lead to very large linear programming models. Therefore, for bounding purposes we also derive several lagrangean based procedures from a directed multicommodity flow formulation and present some computational results taken from a set of instances with up to 40 nodes.     

Designing WDM Optical Networks Using Branch-and-Price

In this paper, we present an exact solution procedure for the design of two-layer wavelength division multiplexing (WDM) optical networks with wavelength changers and bifurcated flows. This design problem closely resembles the traditional multicommodity flow problem, except that in the case of WDM optical networks, we are concerned with the routing of multiple commodities in two network layers. Consequently, the corresponding optimization models have to deal with two types of multicommodity variables defined for each of the network layers. The proposed procedure represents one of the first branch-and-price algorithms for a general WDM optical network setting with no assumptions on the number of logical links that can be established between nodes in the network. We apply our procedure in a computational study with four different network configurations. Our results show that for the three tested network configurations our branch-and-price algorithm provides solutions that are on average less than 5 % from optimality. We also provide a comparison of our branch-and-price algorithm with two simple variants of the upper bounding heuristic procedure HLDA that is commonly used for WDM optical network design.     

Convergent Lagrangian and domain cut method for nonlinear knapsack problems

The nonlinear knapsack problem, which has been widely studied in the OR literature, is a bounded nonlinear integer programming problem that maximizes a separable nondecreasing function subject to separable nondecreasing constraints. In this paper we develop a convergent Lagrangian and domain cut method for solving this kind of problems. The proposed method exploits the special structure of the problem by Lagrangian decomposition and dual search. The domain cut is used to eliminate the duality gap and thus to guarantee the finding of an optimal exact solution to the primal problem. The algorithm is first motivated and developed for singly constrained nonlinear knapsack problems and is then extended to multiply constrained nonlinear knapsack problems. Computational results are presented for a variety of medium- or large-size nonlinear knapsack problems. Comparison results with other existing methods are also reported.     

Lower bounds and duality in the optimal control of distributed systems

We develop a duality theory for problems of minimization of a convex functional on a convex set and apply this theory to optimal control problems with non-differentiable functional and state as well as control constraints. The dual problem is sometimes found to be simpler than the primal problem. The equivalence of the primal and the dual problem is established for problems in which the functional is strongly convex. A posteriori error are also given for such problems.     

Primal-dual proximal point algorithm for linearly constrained convex programming problems

A primal-dual version of the proximal point algorithm is developed for linearly constrained convex programming problems. The algorithm is an iterative method to find a saddle point of the Lagrangian of the problem. At each iteration of the algorithm, we compute an approximate saddle point of the Lagrangian function augmented by quadratic proximal terms of both primal and dual variables. Specifically, we first minimize the function with respect to the primal variables and then approximately maximize the resulting function of the dual variables. The merit of this approach exists in the fact that the latter function is differentiable and the maximization of this function is subject to no constraints. We discuss convergence properties of the algorithm and report some numerical results for network flow problems with separable quadratic costs.     

Dynamic Lagrangian dual and reduced RLT constructs for solving 0–1 mixed-integer programs

In this paper, we consider a dynamic Lagrangian dual optimization procedure for solving mixed-integer 0–1 linear programming problems. Similarly to delayed relax-and-cut approaches, the procedure dynamically appends valid inequalities to the linear programming relaxation as induced by the Reformulation-Linearization Technique (RLT). A Lagrangian dual algorithm that is augmented with a primal solution recovery scheme is applied implicitly to a full or partial first-level RLT relaxation, where RLT constraints that are currently being violated by the primal estimate are dynamically generated within the Lagrangian dual problem, thus controlling the size of the dual space while effectively capturing the strength of the RLT-enhanced relaxation. We present a preliminary computational study to demonstrate the efficacy of this approach.     

Existence, uniqueness, and convergence of the regularized primal-dual central path

In a recent work [J. Castro, J. Cuesta, Quadratic regularizations in an interior-point method for primal block-angular problems, Mathematical Programming, in press (doi:10.1007/s10107-010-0341-2)] the authors improved one of the most efficient interior-point approaches for some classes of block-angular problems. This was achieved by adding a quadratic regularization to the logarithmic barrier. This regularized barrier was shown to be self-concordant, thus fitting the general structural optimization interior-point framework. In practice, however, most codes implement primal-dual path-following algorithms. This short paper shows that the primal-dual regularized central path is well defined, i.e., it exists, it is unique, and it converges to a strictly complementary primal-dual solution.     

Solving nonlinear multicommodity flow problems by the analytic center cutting plane method

The paper deals with nonlinear multicommodity flow problems with convex costs. A decomposition method is proposed to solve them. The approach applies a potential reduction algorithm to solve the master problem approximately and a column generation technique to define a sequence of primal linear programming problems. Each subproblem consists of finding a minimum cost flow between an origin and a destination node in an uncapacited network. It is thus formulated as a shortest path problem and solved with Dijkstra’s d-heap algorithm. An implementation is described that takes full advantage of the supersparsity of the network in the linear algebra operations. Computational results show the efficiency of this approach on well-known nondifferentiable problems and also large scale randomly generated problems (up to 1000 arcs and 5000 commodities). This research has been supported by the Fonds National de la Recherche Scientifique Suisse, grant #12-34002.92, NSERC-Canada and FCAR-Quebec. This research was supported by an Obermann fellowship at the Center for Advanced Studies at the University of Iowa.