Computing exact solution to nonlinear integer programming: Convergent Lagrangian and objective level cut method

In this paper, we propose a convergent Lagrangian and objective level cut method for computing exact solution to two classes of nonlinear integer programming problems: separable nonlinear integer programming and polynomial zero-one programming. The method exposes an optimal solution to the convex hull of a revised perturbation function by successively reshaping or re-confining the perturbation function. The objective level cut is used to eliminate the duality gap and thus to guarantee the convergence of the Lagrangian method on a revised domain. Computational results are reported for a variety of nonlinear integer programming problems and demonstrate that the proposed method is promising in solving medium-size nonlinear integer programming problems.     

Convergent Lagrangian heuristics for nonlinear minimum cost network flows

We consider the separable nonlinear and strictly convex single-commodity network flow problem (SSCNFP). We develop a computational scheme for generating a primal feasible solution from any Lagrangian dual vector; this is referred to as “early primal recovery”. It is motivated by the desire to obtain a primal feasible vector before convergence of a Lagrangian scheme; such a vector is not available from a Lagrangian dual vector unless it is optimal. The scheme is constructed such that if we apply it from a sequence of Lagrangian dual vectors that converge to an optimal one, then the resulting primal (feasible) vectors converge to the unique optimal primal flow vector. It is therefore also a convergent Lagrangian heuristic, akin to those primarily devised within the field of combinatorial optimization but with the contrasting and striking advantage that it is guaranteed to yield a primal optimal solution in the limit. Thereby we also gain access to a new stopping criterion for any Lagrangian dual algorithm for the problem, which is of interest in particular if the SSCNFP arises as a subproblem in a more complex model. We construct instances of convergent Lagrangian heuristics that are based on graph searches within the residual graph, and therefore are efficiently implementable; in particular we consider two shortest path based heuristics that are based on the optimality conditions of the original problem. Numerical experiments report on the relative efficiency and accuracy of the various schemes.     

On a Lagrangian penalty function method for nonlinear programming problems

We modify a Lagrangian penalty function method proposed in [4] for constrained convex mathematical programming problems in order to obtain a geometric rate of convergence. For nonconvex problems we show that a special case of the algorithm in the above paper is still convergent without coercivity and convexity assumptions.On leave from the Institute of Mathematics, Hanoi, by a grant from Alexander-von-Humboldt-Stiftung.     

A unified method for a class of convex separable nonlinear knapsack problems

In this paper, a unified algorithm is proposed for solving a class of convex separable nonlinear knapsack problems, which are characterized by positive marginal cost (PMC) and increasing marginal loss–cost ratio (IMLCR). By taking advantage of these two characteristics, the proposed algorithm is applicable to the problem with equality or inequality constraints. In contrast to the methods based on Karush–Kuhn–Tucker (KKT) conditions, our approach has linear computation complexity. Numerical results are reported to demonstrate the efficacy of the proposed algorithm for different problems.     

A class of nonlinear nonseparable continuous knapsack and multiple-choice knapsack problems

This paper considers a general class of continuous, nonlinear, and nonseparable knapsack problems, special cases of which arise in numerous operations and financial contexts. We develop important properties of optimal solutions for this problem class, based on the properties of a closely related class of linear programs. Using these properties, we provide a solution method that runs in polynomial time in the number of decision variables, while also depending on the time required to solve a particular one-dimensional optimization problem. Thus, for the many applications in which this one-dimensional function is reasonably well behaved (e.g., unimodal), the resulting algorithm runs in polynomial time. We next develop a related solution approach to a class of continuous, nonlinear, and nonseparable multiple-choice knapsack problems. This algorithm runs in polynomial time in both the number of variables and the number of variants per item, while again dependent on the complexity of the same one-dimensional optimization problem as for the knapsack problem. Computational testing demonstrates the power of the proposed algorithms over a commercial global optimization software package.     

A nonlinear Lagrangian for constrained optimization problems

A novel nonlinear Lagrangian is presented for constrained optimization problems with both inequality and equality constraints, which is nonlinear with respect to both functions in problem and Lagrange multipliers. The nonlinear Lagrangian inherits the smoothness of the objective and constraint functions and has positive properties. The algorithm on the nonlinear Lagrangian is demonstrated to possess local and linear convergence when the penalty parameter is less than a threshold (the penalty parameter in the penalty method has to approximate zero) under a set of suitable conditions, and be super-linearly convergent when the penalty parameter is decreased following Lagrange multiplier update. Furthermore, the dual problem based on the nonlinear Lagrangian is discussed and some important properties are proposed, which fail to hold for the dual problem based on the classical Lagrangian. At last, the preliminary and comparing numerical results for several typical test problems by using the new nonlinear Lagrangian algorithm and the other two related nonlinear Lagrangian algorithms, are reported, which show that the given nonlinear Lagrangian is promising.     

A nonlinear augmented Lagrangian for constrained minimax problems

A novel smooth nonlinear augmented Lagrangian for solving minimax problems with inequality constraints, is proposed in this paper, which has the positive properties that the classical Lagrangian and the penalty function fail to possess. The corresponding algorithm mainly consists of minimizing the nonlinear augmented Lagrangian function and updating the Lagrange multipliers and controlling parameter. It is demonstrated that the algorithm converges Q-superlinearly when the controlling parameter is less than a threshold under the mild conditions. Furthermore, the condition number of the Hessian of the nonlinear augmented Lagrangian function is studied, which is very important for the efficiency of the algorithm. The theoretical results are validated further by the preliminary numerical experiments for several testing problems reported at last, which show that the nonlinear augmented Lagrangian is promising.     

Buffer allocation for a class of nonlinear stochastic knapsack problems

In this paper, we examine a class of nonlinear, stochastic knapsack problems which occur in manufacturing, facility or other network design applications.Series, merge-and-split topologies of series-parallelM/M/1/K andM/M/C/K queueing networks with an overall buffer constraint bound are examined. Bounds on the objective function are proposed and a sensitivity analysis is utilized to quantify the effects of buffer variations on network performance measures.     

A branch and search algorithm for a class of nonlinear knapsack problems

This paper discusses a class of nonlinear knapsack problems where the objective function is quadratic. The method is a branch and search procedure which includes an efficient algorithm to find the continuous (relaxed) solution and a reduction rule which computes tight lower and upper bounds on the integer variables.     

Exact solution of a class of nonlinear knapsack problems

We consider a class of nonlinear knapsack problems with applications in service systems design and facility location problems with congestion. We provide two linearizations and their respective solution approaches. The first is solved directly using a commercial solver. The second is a piecewise linearization that is solved by a cutting plane method.     

Convergent computational method for relaxed optimal control problems

A class of relaxed optimal control problems for ordinary differential equations with a state-space constraint is considered. The discretization by the control parametrization method, formerly proposed by Teo and Goh (Refs. 1, 2), is modified by admitting a tolerance in the state constraint, which enables one to prove a conditional convergence under certain additional qualification on the dynamics. Also, a counterexample is constructed, showing that the original, nonmodified discretization need not approximate the continuous problem.The author is grateful to Professor K. L. Teo for useful comments on this paper.     

A proximal augmented Lagrangian method for equilibrium problems

Considering a recently proposed proximal point method for equilibrium problems, we construct an augmented Lagrangian method for solving the same problem in reflexive Banach spaces with cone constraints generating a strongly convergent sequence to a certain solution of the problem. This is an inexact hybrid method meaning that at a certain iterate, a solution of an unconstrained equilibrium problem is found, allowing a proper error bound, followed by a Bregman projection of the initial iterate onto the intersection of two appropriate halfspaces. Assuming a set of reasonable hypotheses, we provide a full convergence analysis.     

On stationary points of the implicit Lagrangian for nonlinear complementarity problems

Mangasarian and Solodov have recently introduced an unconstrained optimization problem whose global minima are solutions of the nonlinear complementarity problem (NCP). In this paper, we show that, if the mapping involved in NCP has a positive-definite Jacobian, then any stationary point of the optimization problem actually solves NCP. We also discuss a descent method for solving the unconstrained optimization problem.The authors are indebted to a referee for a helpful suggestion that led them to develop the descent method described in Section 3. They are grateful to Professor F. Facchinei, who kindly pointed out an error in the proof of Theorem 2.3 in an earlier version of the paper. The also thank Professor P. Tseng for a discussion on Theorem 3.1.     

A deterministic Lagrangian particle separation-based method for advective-diffusion problems

A simple and robust Lagrangian particle scheme is proposed to solve the advective-diffusion transport problem. The scheme is based on relative diffusion concepts and simulates diffusion by regulating particle separation. This new approach generates a deterministic result and requires far less number of particles than the random walk method. For the advection process, particles are simply moved according to their velocity. The general scheme is mass conservative and is free from numerical diffusion. It can be applied to a wide variety of advective-diffusion problems, but is particularly suited for ecological and water quality modelling when definition of particle attributes (e.g., cell status for modelling algal blooms or red tides) is a necessity. The basic derivation, numerical stability and practical implementation of the NEighborhood Separation Technique (NEST) are presented. The accuracy of the method is demonstrated through a series of test cases which embrace realistic features of coastal environmental transport problems. Two field application examples on the tidal flushing of a fish farm and the dynamics of vertically migrating marine algae are also presented.     

A new augmented Lagrangian function for inequality constraints in nonlinear programming problems

In this paper, a new augmented Lagrangian function is introduced for solving nonlinear programming problems with inequality constraints. The relevant feature of the proposed approach is that, under suitable assumptions, it enables one to obtain the solution of the constrained problem by a single unconstrained minimization of a continuously differentiable function, so that standard unconstrained minimization techniques can be employed. Numerical examples are reported.     

Hybrid rounding techniques for knapsack problems

We address the classical knapsack problem and a variant in which an upper bound is imposed on the number of items that can be selected. We show that appropriate combinations of rounding techniques yield novel and more powerful ways of rounding. Moreover, we present a linear-storage polynomial time approximation scheme (PTAS) and a fully polynomial time approximation scheme (FPTAS) that compute an approximate solution, of any fixed accuracy, in linear time. These linear complexity bounds give a substantial improvement of the best previously known polynomial bounds [A. Caprara, et al., Approximation algorithms for knapsack problems with cardinality constraints, European J. Oper. Res. 123 (2000) 333-345].     

A Convergent Lagrangian Discretization for a Nonlinear Fourth-Order Equation

A fully discrete Lagrangian scheme for numerical solution of the nonlinear fourth-order DLSS equation in one space dimension is analyzed. The discretization is based on the equation’s gradient flow structure in the \(L^2\)-Wasserstein metric. By construction, the discrete solutions are strictly positive and mass conserving. A further key property is that they dissipate both the Fisher information and the logarithmic entropy. Our main result is a proof of convergence of fully discrete to weak solutions in the limit of vanishing mesh size. Convergence is obtained for arbitrary nonnegative, possibly discontinuous initial data with finite entropy, without any CFL-type condition. The key estimates in the proof are derived from the dissipations of the two Lyapunov functionals. Numerical experiments illustrate the practicability of the scheme.