A new penalty parameter for linearly constrained 0–1 quadratic programming problems

The equivalence between the linearly constrained 0–1 quadratic programming problem and the continuous quadratic programming problem is studied in this note. Specifically, we show that the existing penalty parameter from the literature can be further improved.     

Concave extensions for nonlinear 0–1 maximization problems

A well-known linearization technique for nonlinear 0–1 maximization problems can be viewed as extending any polynomial in 0–1 variables to a concave function defined on [0, 1] ⁿ . Some properties of this standard concave extension are investigated. Polynomials for which the standard extension coincides with the concave envelope are characterized in terms of integrality of a certain polyhedron or balancedness of a certain matrix. The standard extension is proved to be identical to another type of concave extension, defined as the lower envelope of a class of affine functions majorizing the given polynomial.     

Speeding up IP-based algorithms for constrained quadratic 0–1 optimization

In many practical applications, the task is to optimize a non-linear objective function over the vertices of a well-studied polytope as, e.g., the matching polytope or the travelling salesman polytope (TSP). Prominent examples are the quadratic assignment problem and the quadratic knapsack problem; further applications occur in various areas such as production planning or automatic graph drawing. In order to apply branch-and-cut methods for the exact solution of such problems, the objective function has to be linearized. However, the standard linearization usually leads to very weak relaxations. On the other hand, problem-specific polyhedral studies are often time-consuming. Our goal is the design of general separation routines that can replace detailed polyhedral studies of the resulting polytope and that can be used as a black box. As unconstrained binary quadratic optimization is equivalent to the maximum-cut problem, knowledge about cut polytopes can be used in our setting. Other separation routines are inspired by the local cuts that have been developed by Applegate, Bixby, Chvátal and Cook for faster solution of large-scale traveling salesman instances. Finally, we apply quadratic reformulations of the linear constraints as proposed by Helmberg, Rendl and Weismantel for the quadratic knapsack problem. By extensive experiments, we show that a suitable combination of these methods leads to a drastic speedup in the solution of constrained quadratic 0–1 problems. We also discuss possible generalizations of these methods to arbitrary non-linear objective functions.     

A compact variant of the QCR method for quadratically constrained quadratic 0–1 programs

Quadratic Convex Reformulation (QCR) is a technique that was originally proposed for quadratic 0–1 programs, and then extended to various other problems. It is used to convert non-convex instances into convex ones, in such a way that the bound obtained by solving the continuous relaxation of the reformulated instance is as strong as possible. In this paper, we focus on the case of quadratically constrained quadratic 0–1 programs. The variant of QCR previously proposed for this case involves the addition of a quadratic number of auxiliary continuous variables. We show that, in fact, at most one additional variable is needed. Some computational results are also presented.     

A note for tightening 0–1 models

We describe two approaches for 0–1 program model tightening that are based on the coefficient increasing and reduction methods proposed in Dietrich, Escudero and Chance (1993). We present some characterizations for the new formulations to be tighter than the original model. It can be shown that tighter models can be obtained even when applying any of both approaches to a redundant constraint; see Escudero and Muñoz (1998). We also present some situations where these approaches cannot be applied.     

A modified Hestenes–Stiefel projection method for constrained nonlinear equations and its linear convergence rate

The Hestenes–Stiefel (HS) method is an efficient method for solving large-scale unconstrained optimization problems. In this paper, we extend the HS method to solve constrained nonlinear equations, and propose a modified HS projection method, which combines the modified HS method proposed by Zhang et al. with the projection method developed by Solodov and Svaiter. Under some mild assumptions, we show that the new method is globally convergent with an Armijo line search. Moreover, the R-linear convergence rate of the new method is established. Some preliminary numerical results show that the new method is efficient even for large-scale constrained nonlinear equations.     

Solving 0–1 semidefinite programs for distributionally robust allocation of surgery blocks

We allocate surgery blocks to operating rooms (ORs) under random surgery durations. Given unknown distribution of the duration of each block, we investigate distributionally robust (DR) variants of two types of stochastic programming models using a moment-based ambiguous set. We minimize the total cost of opening ORs and allocating surgery blocks, while constraining OR overtime via chance constraints and via an expected penalty cost in the objective function, respectively in the two types of models. Following conic duality, we build equivalent 0–1 semidefinite programming (SDP) reformulations of the DR models and solve them using cutting-plane algorithms. For the DR chance-constrained model, we also derive a 0–1 second-order conic programming approximation to obtain less conservative solutions. We compare different models and solution methods by testing randomly generated instances. Our results show that the DR chance-constrained model better controls average and worst-case OR overtime, as compared to the stochastic programming and DR expected-penalty-based models. Our cutting-plane algorithms also outperform standard optimization solvers and efficiently solve 0–1 SDP formulations.     

A variable fixing version of the two-block nonlinear constrained Gauss–Seidel algorithm for \ell _1-regularized least-squares

The problem of finding sparse solutions to underdetermined systems of linear equations is very common in many fields as e.g. signal/image processing and statistics. A standard tool for dealing with sparse recovery is the \(\ell _1\) -regularized least-squares approach that has recently attracted the attention of many researchers. In this paper, we describe a new version of the two-block nonlinear constrained Gauss–Seidel algorithm for solving \(\ell _1\) -regularized least-squares that at each step of the iteration process fixes some variables to zero according to a simple active-set strategy. We prove the global convergence of the new algorithm and we show its efficiency reporting the results of some preliminary numerical experiments.     

Gauss–Newton methods with approximate projections for solving constrained nonlinear least squares problems

This paper is concerned with algorithms for solving constrained nonlinear least squares problems. We first propose a local Gauss–Newton method with approximate projections for solving the aforementioned problems and study, by using a general majorant condition, its convergence results, including results on its rate. By combining the latter method and a nonmonotone line search strategy, we then propose a global algorithm and analyze its convergence results. Finally, some preliminary numerical experiments are reported in order to illustrate the advantages of the new schemes.     

A scenario decomposition algorithm for 0–1 stochastic programs

We propose a scenario decomposition algorithm for stochastic 0–1 programs. The algorithm recovers an optimal solution by iteratively exploring and cutting-off candidate solutions obtained from solving scenario subproblems. The scheme is applicable to quite general problem structures and can be implemented in a distributed framework. Illustrative computational results on standard two-stage stochastic integer programming and nonlinear stochastic integer programming test problems are presented.     

A Lagrange–Newton algorithm for sparse nonlinear programming

Mathematical Programming - The sparse nonlinear programming (SNP) problem has wide applications in signal and image processing, machine learning and finance, etc. However, the computational...     

Interpolating between constrained Li–Yau and Chow–Hamilton Harnack inequalities for a nonlinear parabolic equation

We establish a one-parameter family of Harnack inequalities connecting the constrained trace Li–Yau differential Harnack inequality for a nonlinear parabolic equation to the constrained trace Chow–Hamilton Harnack inequality for this nonlinear equation with respect to evolving metrics related to the Ricci flow on a 2-dimensional closed manifold. This result can be regarded as a nonlinear version of the previous work of Y. Zheng and the author [J.-Y. Wu, Y. Zheng, Interpolating between constrained Li–Yau and Chow–Hamilton Harnack inequalities on a surface, Arch. Math., 94 (2010) 591–600].     

A prediction–correction-based primal–dual hybrid gradient method for linearly constrained convex minimization

Numerical Algorithms - The primal–dual hybrid gradient (PDHG) method has been widely used for solving saddle point problems emerged in imaging processing. In particular, PDHG can be used to...     

A primal–dual interior point method for nonlinear semidefinite programming

This paper is concerned with a primal–dual interior point method for solving nonlinear semidefinite programming problems. The method consists of the outer iteration (SDPIP) that finds a KKT point and the inner iteration (SDPLS) that calculates an approximate barrier KKT point. Algorithm SDPLS uses a commutative class of Newton-like directions for the generation of line search directions. By combining the primal barrier penalty function and the primal–dual barrier function, a new primal–dual merit function is proposed. We prove the global convergence property of our method. Finally some numerical experiments are given.     

Subgradient projection algorithms for constrained nonsmooth optimization II: nonlinear constraints†

In this paper, a kind of subgradient projection algorithms is established for minimizing a locally Lipschitz continuous function subject to nonlinearly smooth constraints, which is based on the idea to get a feasible and strictly descent direction by combining the ?-subgradient projection direction that attempts to satisfy the Kuhn-Tucker conditions with one corrected direction produced by a linear programming subproblem. The algorithm avoids the zigzagging phenomenon and converges to Kuhn-Tucker points, due to using the c.d.f. maps of Polak and Mayne (1985), ?active constraints and ?adjusted rules     

Hierarchical location–allocation models for congested systems

In this paper we address the issue of locating hierarchical facilities in the presence of congestion. Two hierarchical models are presented, where lower level servers attend requests first, and then, some of the served customers are referred to higher level servers. In the first model, the objective is to find the minimum number of servers and their locations that will cover a given region with a distance or time standard. The second model is cast as a maximal covering location (MCL) formulation. A heuristic procedure is then presented together with computational experience. Finally, some extensions of these models that address other types of spatial configurations are offered.