Additional Linear Constraints Research Articles

Mathematical models based on decision hypergraphs for designing a storage cabinet

We study the problem of designing a cabinet made up of a set of shelves that contain compartments whose contents slide forward on opening. Considering a set of items candidate to be stored in the cabinet over a given time horizon, the problem is to design a set of shelves and a set of compartments on each shelf, and select the items to insert into the compartments. The objective is to maximize the sum of the profits of the selected items. We call our problem the Storage Cabinet Physical Design (SCPD) problem. The SCPD problem combines a two-staged two-dimensional knapsack problem for designing the shelves and compartments with a set of temporal knapsack problems for selecting and assigning items to compartments. We formalize the SCPD problem and formulate it as a maximum cost flow problem in a decision hypergraph with additional linear constraints. To reduce the size of this model, we break symmetries, generalize graph compression techniques and exploit dominance rules for precomputing subproblem solutions. We also present a set of valid inequalities to improve the linear relaxation of the model. We empirically show that solving the arc-flow model with our enhancements outperforms solving a compact mixed integer linear programming formulation of the SCPD problem.

Supervised Feature Selection via Collaborative Neurodynamic Optimization.

As a crucial part of machine learning and pattern recognition, feature selection aims at selecting a subset of the most informative features from the set of all available features. In this article, supervised feature selection is at first formulated as a mixed-integer optimization problem with an objective function of weighted feature redundancy and relevancy subject to a cardinality constraint on the number of selected features. It is equivalently reformulated as a bound-constrained mixed-integer optimization problem by augmenting the objective function with a penalty function for realizing the cardinality constraint. With additional bilinear and linear equality constraints for realizing the integrality constraints, it is further reformulated as a bound-constrained biconvex optimization problem with two more penalty terms. Two collaborative neurodynamic optimization (CNO) approaches are proposed for solving the formulated and reformulated feature selection problems. One of the proposed CNO approaches uses a population of discrete-time recurrent neural networks (RNNs), and the other use a pair of continuous-time projection networks operating concurrently on two timescales. Experimental results on 13 benchmark datasets are elaborated to substantiate the superiority of the CNO approaches to several mainstream methods in terms of average classification accuracy with three commonly used classifiers.

Slater conditions without interior points for programs in Lebesgue spaces with pointwise bounds and finitely many constraints

We consider optimization problems in Lebesgue spaces with pointwise box constraints and finitely many additional linear constraints. We prove that the existence of a Slater point which lies strictly between the pointwise bounds and which satisfies the linear constraints is sufficient for the existence of Lagrange multipliers. Surprisingly, the Slater point is also necessary for the existence of Lagrange multipliers in a certain sense. We also demonstrate how to handle additional finitely many nonlinear constraints.

Chiral Matter Multiplicities and Resolution-Independent Structure in 4D F-Theory Models

Motivated by questions related to the landscape of flux compactifications, we combine new and existing techniques into a systematic, streamlined approach for computing vertical fluxes and chiral matter multiplicities in 4D F-theory models. A central feature of our approach is the conjecturally resolution-independent intersection pairing of the vertical part of the integer middle cohomology of smooth elliptic Calabi–Yau fourfolds, relevant for computing chiral indices and related aspects of 4D F-theory flux vacua. We illustrate our approach by analyzing vertical flux backgrounds for F-theory models with simple, simply-laced gauge groups and generic matter content, as well as models with U(1)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext {U}}(1)$$\\end{document} gauge factors. We explicitly analyze resolutions of these F-theory models in which the elliptic fiber is realized as a cubic in P2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathbb {P}}^2$$\\end{document} over an arbitrary (e.g., not necessarily toric) smooth base, and confirm the independence of the intersection pairing of the vertical part of the middle cohomology for the resolutions we study. In each model, we find that vertical flux backgrounds can produce nonzero multiplicities for a spanning set of anomaly-free chiral matter field combinations, suggesting that F-theory geometry imposes no additional linear constraints on allowed matter representations beyond those implied by 4D anomaly cancellation.

A wavefront adaptive sensing beamformer for ocean acoustic waveguides.

This paper addresses robust adaptive beamforming for passive sonar in uncertain, shallow-water environments. Conventional beamforming is still common in passive sonar because adaptive beamformers suffer from signal mismatch in complex multipath environments. Existing approaches to robust adaptive beamforming try to model and account for the uncertainty in the beamformer's hypothesized signal subspace by using additional linear or quadratic constraints. Doing so, however, reduces the adaptivity of the beamformer and is prone to insufficiently suppressing interference. Instead, this paper uses blind source separation methods to adaptively estimate the complex spatial wavefronts of both targets and interference without requiring detailed physical modeling of the channel. By exploiting the different temporal spectra and/or frequency-selective multipath fading of targets and interference, this approach constructs a "signal-free" covariance matrix without imposing directional gain constraints. In doing so, the wavefront adaptive sensing (WAS) beamformer is able to separate targets from strong interference that is within the conventional beam width of the target. Simulation results in a realistic shallow-water channel are presented as well as results using the SWellEx96 S59 data with an injected target to show that the proposed WAS beamformer outperforms conventional and minimum variance adaptive beamformers in a shallow-water scenario.

The biobjective minimum‐cost perfect matching problem and Chinese postman problem

AbstractIn this paper, we address the biobjective versions of the perfect matching problem (PMP) and the Chinese postman problem (CPP). Both problems are solved by means of integer formulations or separating blossom inequalities, exploiting the PMP relationship with the CPP. In both cases, we first find the set of supported nondominated solutions and then we use them to obtain the nonsupported ones. The set of supported nondominated solutions are obtained solving scalarized integer formulations. To obtain the sets of nonsupported solutions, we resort to solving lexicographic problems based on adding additional linear constraints to the original problems. For this reason, we also characterize the combinatorial structure of the PMP vertices with one or two additional constraints. We also investigate when it is possible to use the PMP to solve CPP in the biobjective case. We report computational experiments comparing the different approaches and formulations based on different types of graphs with up to 700 nodes.

Fisher markets with linear constraints: Equilibrium properties and efficient distributed algorithms

The Fisher market is one of the most fundamental models for resource allocation. However, Fisher markets are less amenable for resource allocation settings when agents have additional linear constraints beyond the budget constraints of buyers and the capacity constraints of goods. Thus, in this work, we introduce a modified Fisher market, where agents may have additional linear constraints, and study the properties of the resulting equilibria. To set equilibrium prices, we introduce a budget-adjusted social optimization problem (BA-SOP), whose optimal dual variables correspond to the equilibrium prices. Since solving BA-SOP can be computationally intensive and requires centralized knowledge of all agents' utilities, we propose a new class of distributed algorithms based on the Alternating Direction Method of Multipliers (ADMM) to compute equilibrium prices. Our ADMM approach has strong convergence guarantees and provides a general-purpose method for computing market equilibria for Fisher markets with homogeneous linear constraints and classical Fisher markets.

Construction of quite interpretable non-elementary linear regression models

Subject of research: mixed-integer 0-1 linear programming problem for choosing optimal structures for non-elementary linear regression models.
 Purpose of research: integrate into the mixed-integer 0-1 linear programming problem additional constraints that will guarantee the construction of quite interpretable non-elementary linear regressions.
 Methods of research: regression analysis, mathematical programming, method of successive increase the absolute contributions of variables to the general determination.
 Object of research: non-elementary linear regression models.
 Main results of research: in the mixed-integer 0-1 linear programming problem, designed to construct non-elementary regressions, additional linear constraints on the absolute contributions of variables to the general determination are integrated, allowing you to control both the contributions themselves and multicollinearity in the model. It is shown how it is necessary to regulate these constraints so that the non-elementary linear regression obtained as a result of solving the problem is quite interpretable. The proposed mathematical apparatus was used to model railroad freight transportation in the Tyumen region. An interpretation of the obtained high-precision and quite interpretable non-elementary linear regression is given.

Improving Color Constancy Using Chromaticity-Line Prior

Color constancy is the ability to remove the effect of illumination on color. Since color constancy is an ill-posed problem, many methods have been proposed based on assumptions to constraint the solution space. However, most existing assumptions require specular pixels or abundant colors, and fail to produce satisfactory results for different scenarios. According to extensive experiments, we observe that the chromaticity distribution of pixels within main color under canonical illumination, which we called canonical pixels, is linear and can also locate the position of illumination under the non-canonical illumination. Therefore, this paper proposes a chromaticity-line prior (CLP) as an additional linear constraint on the ill-posed problem of color constancy. In the calculation of CLP, the simple linear iterative clustering is firstly employed to segment an image into several super-pixel blocks. And the random sampling consensus is utilized to remove non-primary color points and fit the chromaticity-line. Based on the proposed CLP, a color constancy algorithm is implemented correspondingly. Since the main idea of the CLP is to extract the canonical pixels, which is the inherent property of image, the proposed CLP is more general and adaptive in real scenes. The experiments on two public datasets demonstrate that the proposed algorithm not only outperforms state-of-the-art learning-free algorithms, but also achieves results that are competitive to those of learning-based algorithms.

Robust Adaptive Beamforming via Worst-Case SINR Maximization With Nonconvex Uncertainty Sets

This paper considers a formulation of the robust adaptive beamforming (RAB) problem based on worst-case signal-to-interference-plus-noise ratio (SINR) maximization with a nonconvex uncertainty set for the steering vectors. The uncertainty set consists of a similarity constraint and a (nonconvex) double-sided ball constraint. The worst-case SINR maximization problem is turned into a quadratic matrix inequality (QMI) problem using the strong duality of semidefinite programming. Then a linear matrix inequality (LMI) relaxation for the QMI problem is proposed, with an additional valid linear constraint. Necessary and sufficient conditions for the tightened LMI relaxation problem to have a rank-one solution are established. When the tightened LMI relaxation problem still has a high-rank solution, the LMI relaxation problem is further restricted to become a bilinear matrix inequality (BLMI) problem. We then propose an iterative algorithm to solve the BLMI problem that finds an optimal/suboptimal solution for the original RAB problem by solving the BLMI formulations. To validate our results, simulation examples are presented to demonstrate the improved array output SINR of the proposed robust beamformer.

Stokes, Gibbs, and Volume Computation of Semi-Algebraic Sets

We consider the problem of computing the Lebesgue volume of compact basic semi-algebraic sets. In full generality, it can be approximated as closely as desired by a converging hierarchy of upper bounds obtained by applying the Moment-SOS (sums of squares) methodology to a certain infinite-dimensional linear program (LP). At each step one solves a semidefinite relaxation of the LP which involves pseudo-moments up to a certain degree. Its dual computes a polynomial of same degree which approximates from above the discontinuous indicator function of the set, hence with a typical Gibbs phenomenon which results in a slow convergence of the associated numerical scheme. Drastic improvements have been observed by introducing in the initial LP additional linear moment constraints obtained from a certain application of Stokes’ theorem for integration on the set. However and so far there was no rationale to explain this behavior. We provide a refined version of this extended LP formulation. When the set is the smooth super-level set of a single polynomial, we show that the dual of this refined LP has an optimal solution which is a continuous function. Therefore in this dual one now approximates a continuous function by a polynomial, hence with no Gibbs phenomenon, which explains and improves the already observed drastic acceleration of the convergence of the hierarchy. Interestingly, the technique of proof involves recent results on Poisson’s partial differential equation (PDE).

Exactness Conditions for Semidefinite Programming Relaxations of Generalization of the Extended Trust Region Subproblem

The extended trust region subproblem (ETRS) of minimizing a quadratic objective over the unit ball with additional linear constraints has attracted a lot of attention in the last few years because of its theoretical significance and wide spectra of applications. Several sufficient conditions to guarantee the exactness of its semidefinite programming (SDP) relaxation have been recently developed in the literature. In this paper, we consider a generalization of the extended trust region subproblem (GETRS), in which the unit ball constraint in the ETRS is replaced by a general, possibly nonconvex, quadratic constraint, and the linear constraints are replaced by a conic linear constraint. We derive sufficient conditions for guaranteeing the exactness of the SDP relaxation of the GETRS under mild assumptions. Our main tools are two classes of convex relaxations for the GETRS based on either a simultaneous diagonalization transformation of the quadratic forms or linear combinations of the quadratic forms. We also compare our results to the existing sufficient conditions in the literature. Finally, we extend our results to derive a new sufficient condition for the exactness of the SDP relaxation of general diagonal quadratically constrained quadratic programs, where each quadratic function is associated with a diagonal matrix. Funding: This work was supported by the National Natural Science Foundation of China [Grants 11801087, 12171100, 72161160340]; Hong Kong Research Grants Council [Grants 14213716 and 14202017]; and Natural Science Foundation of Shanghai [Grant 22ZR1405100].

A Spectral Approach to Network Design

We present a spectral approach to design approximation algorithms for network design problems. We observe that the underlying mathematical questions are the spectral rounding problems, which were studied in spectral sparsification and in discrepancy theory. We extend these results to incorporate additional non-negative linear constraints, and show that they can be used to significantly extend the scope of network design problems that can be solved. Our algorithm for spectral rounding is an iterative randomized rounding algorithm based on the regret minimization framework. In some settings, this provides an alternative spectral algorithm to achieve constant factor approximation for the classical survivable network design problem, and partially answers a question of Bansal about survivable network design with concentration property. We also show many other applications of the spectral rounding results, including weighted experimental design and additive spectral sparsification.

Robust AC Transmission Expansion Planning Using a Novel Dual-Based Bi-Level Approach

The rapid integration of Renewable Energy Sources (RESs) strengthens the need for a power network that can robustly handle the system's uncertain scenarios. Thus, this paper proposes the first nonlinear novel dual based bi-level approach for robust AC Transmission Expansion Planning (TEP) plans with uncertainties in RES generations and loads. It utilizes a convex relaxation and is solved by Benders Decomposition (BD), where the master determines the robust AC TEP plan. The novel dual slave model for the second level of BD circumvents the issues in using the conventional conic dual theory and aids in the worst-case realization of uncertainties using additional novel constraints. The novel dual slave is solved using Interior Point Method (IPM), as it is not a mixed-integer problem. The proposed work also includes additional linear constraints to reduce the BD's slow convergence and direct the master towards optimality. The robustness of the AC TEP plans is verified by Monte-Carlo Simulation (MCS) of the actual nonlinear and non-convex AC Optimal Power Flow (OPF). The effect of the budget of uncertainty on the AC TEP plans is also investigated. A comparison with the results of a previous work reveals the superiority of the proposed work.

A penalty decomposition approach for multi-objective cardinality-constrained optimization problems

In this manuscript, we consider multi-objective optimization problems with a cardinality constraint on the vector of decision variables and additional linear constraints. For this class of problems, we analyse necessary and sufficient conditions of Pareto optimality. We afterwards propose a Penalty Decomposition type algorithm, exploiting multi-objective descent methods, to tackle the aforementioned family of problems. We conduct a rigorous convergence analysis for the proposed method, where we prove that the produced sequence of points has limit points, each one being feasible and satisfying first-order optimality conditions. Numerical computational experiments, carried out on instances of relevant real-world problems such as sparse mean/variance portfolio selection and sparse regularized logistic regression, in their multi-objective formulation, show that the proposed procedure is effective at finding solutions forming good Pareto sets approximations.

Restricted estimation of distributed lag model from a Bayesian point of view

This article addresses the issue of multicollinearity for the distributed lag model via Bayesian approach. We introduce the Bayes Almon ridge estimator under this approach by employing prior information. Specifically, the resulting estimator is a new Bayesian estimator that is the mean of posterior density function for the coefficient of the Almon model. Also, a restricted version of this estimator is proposed by using Lagrange multipliers to optimize the coefficients under the additional exact linear constraints. In mathematical sense, theoretical comparisons are carried out by employing mean squared error utilizing matrix theory. Additionally, we investigate a selection method for the biasing parameter of the new estimators with the help of the mean squared error comparisons. To test the theoretical suggestions, we perform a real-life data analysis as a means of applied mathematics. Moreover, we benefit from Monte Carlo simulation techniques where we use several levels of different parameters. The outcomes of numerical example and simulation study favor the new estimators and, hence we succeed in eliminating the multicollinearity.

Embedding of complete graphs in broken Chimera graphs

In order to solve real-world combinatorial optimization problems with a D-Wave quantum annealer, it is necessary to embed the problem at hand into the D-Wave hardware graph, namely Chimera or Pegasus. Most hard real-world problems exhibit a strong connectivity. For the worst-case scenario of a complete graph, there exists an efficient solution for the embedding into the ideal Chimera graph. However, since real machines almost always have broken qubits, it is necessary to find an embedding into the broken hardware graph. We present a new approach to the problem of embedding complete graphs into broken Chimera graphs. This problem can be formulated as an optimization problem, more precisely as a matching problem with additional linear constraints. Although being NP-hard in general, it is fixed-parameter tractable in the number of inaccessible vertices in the Chimera graph. We tested our exact approach on various instances of broken hardware graphs, both related to real hardware and randomly generated. For fixed runtime, we were able to embed larger complete graphs compared to previous, heuristic approaches. As an extension, we developed a fast heuristic algorithm which enables us to solve even larger instances. We compared the performance of our heuristic and exact approaches.

Isogeometric collocation method with intuitive derivative constraints for PDE-based analysis-suitable parameterizations

This paper presents a general formulation of an isogeometric collocation method (IGA-C) for the parameterization of computational domains for the isogeometric analysis (IGA) using non-uniform rational B-splines (NURBS). The boundary information of desired computational domains for IGA is imposed as a Dirichlet boundary condition on a simple and smooth initial parameterization of an initial computational domain, and the final parameterization is produced based on the numerical solution of a partial differential equation (PDE) that is solved using the IGA-C method. In addition, we apply intuitive derivative constraints while solving the PDE to achieve desired properties of smoothness and uniformity of the resulting parameterization. While one may use any general PDE with any constraint, the PDEs and additional constraints selected in our case are such that the resulting solution can be efficiently solved through a system of linear equations with or without additional linear constraints. This approach is different from typical existing parameterization methods in IGA that are often solved through an expensive nonlinear optimization process. The results show that the proposed method can efficiently produce satisfactory analysis-suitable parameterizations.

A new general class of RC association models: Estimation and main properties

The paper introduces a new class of row-column (RC) association models for contingency tables by allowing the user to select both the scale on which interactions are measured as in Kateri and Papaioannou (1994) and the type of logit (local, global, continuation) suitable for the row and column classification variables as in Bartolucci and Forcina (2002). These choices determine the matrix of interactions which is subjected to rank constraints. Examples are provided where the new models fit substantially better than traditional ones; an intuitive explanation for this behavior is outlined and supported by numerical investigations. An extension of the optimality property in Kateri and Papaioannou (1994) is derived, leading to a general representation theorem and reconstruction formulas for the joint probabilities. These results are the key to show that, given marginal logits, the generalized interactions introduced in this paper determine uniquely the bivariate distribution; in addition, for each pair of logit types, we establish which kind of positive association is implied by our extended interactions being non negative. Quick model selection within this wide class can be performed by an efficient algorithm for computing maximum likelihood estimates which allows for additional linear constraints both on marginal logits and interactions.

An efficient algorithm for large-scale extended trust-region subproblems with non-intersecting linear constraints

In this paper, we study the extended trust-region subproblem in which the trust-region intersects the ball with m linear inequality constraints (m-eTRS). We assume that the linear constraints do not intersect inside the ball. We show that the optimal solution of m-eTRS can be found by solving one TRS, computing the local non-global minimizer of TRS if it exists and solving at most two TRSs with an additional linear equality constraint (1-eqTRS). Both TRS and (1-eqTRS) are polynomially and efficiently solvable, thus the new algorithm significantly improves over the SOCP/SDP relaxation of Burer and Yang [Math Program 149(1-2):253–264, 2015]. on two classes of test problems, the efficiency of the proposed approach is compared with the SOCP/SDP relaxation and branch and bound algorithm of Beck and Pan [J Global Optim 69(2):309–342, 2017].