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Jon Lee

Publications and source records attributed to Jon Lee.

At least 37 records · Page 2Linked to original sources
arXiv2026

The dual-path fixing strategy and its application to the set-covering problem

We introduce the dual-path fixing strategy to exploit dual algorithms for solving relaxations of mixed-integer nonlinear-optimization problems. Such dual algorithms are naturally applied in the context of branch-and-bound, and eventual impact on the success of branch-and-bound is our strong motivation. Our fixing strategy aims to be more powerful than the common strategy of fixing variables based on a single dual-feasible solution (e.g., standard reduced-cost fixing for mixed-integer linear optimization), but to be much faster than ``strong fixing'', essentially requiring no more time than that of the dual algorithm that we exploit. We have successfully tested our ideas on mixed-integer linear-optimization set-covering instances from the literature, in the context of the dual-simplex method applied to the continuous relaxations.

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arXiv2025

Recent Advances in Maximum-Entropy Sampling

In 2022, we published the book Maximum-Entropy Sampling: Algorithms and Application (Springer). Since then, there have been several notable advancements on this topic. In this manuscript, we survey some recent highlights.

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arXiv2025

On a geometric graph-covering problem related to optimal safety-landing-site location

We propose integer-programming formulations for an optimal safety-landing site (SLS) location problem that arises in the design of urban air-transportation networks. We first develop a set-cover based approach for the case where the candidate location set is finite and composed of points, and we link the problems to solvable cases that have been studied. We then use a mixed-integer second-order cone program to model the situation where the locations of SLSs are restricted to convex sets only. Finally, we introduce strong fixing, which we found to be very effective in reducing the size of integer programs.

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arXiv2025

Good and Fast Row-Sparse ah-Symmetric Reflexive Generalized Inverses

We present several algorithms aimed at constructing sparse and structured sparse (row-sparse) generalized inverses, with application to the efficient computation of least-squares solutions, for inconsistent systems of linear equations, in the setting of multiple right-hand sides and a rank-deficient constraint matrix. Leveraging our earlier formulations to minimize the 1- and 2,1- norms of generalized inverses that satisfy important properties of the Moore-Penrose pseudoinverse, we develop efficient and scalable ADMM algorithms to address these norm-minimization problems and to limit the number of nonzero rows in the solution. We establish a 2,1-norm approximation result for a local-search procedure that was originally designed for 1-norm minimization, and we compare the ADMM algorithms with the local-search procedure and with general-purpose optimization solvers.

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arXiv2024

Computing Experiment-Constrained D-Optimal Designs

In optimal experimental design, the objective is to select a limited set of experiments that maximizes information about unknown model parameters based on factor levels. This work addresses the generalized D-optimal design problem, allowing for nonlinear relationships in factor levels. We develop scalable algorithms suitable for cases where the number of candidate experiments grows exponentially with the factor dimension, focusing on both first- and second-order models under design constraints. Particularly, our approach integrates convex relaxation with pricing-based local search techniques, which can provide upper bounds and performance guarantees. Unlike traditional local search methods, such as the ``Fedorov exchange" and its variants, our method effectively accommodates arbitrary side constraints in the design space. Furthermore, it yields both a feasible solution and an upper bound on the optimal value derived from the convex relaxation. Numerical results highlight the efficiency and scalability of our algorithms, demonstrating superior performance compared to the state-of-the-art commercial software, JMP

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arXiv2024

On disjunction convex hulls by lifting

We study the natural extended-variable formulation for the disjunction of $n+1$ polytopes in $\mathbb{R}^d$. We demonstrate that the convex hull $D$ in the natural extended-variable space $\mathbb{R}^{d+n}$ is given by full optimal big-M lifting (i) when $d\leq 2$ (and that it is not generally true for $d\geq 3$), and also (ii) under some technical conditions, when the polytopes have a common facet-describing constraint matrix, for arbitrary $d\geq 1$ and $n\geq 1$. We give a broad family of examples with $d\geq 3$ and $n=1$, where the convex hull is not described after employing all full optimal big-M lifting inequalities, but it is described after one round of MIR inequalities. Additionally, we give some general results on the polyhedral structure of $D$, and we demonstrate that all facets of $D$ can be enumerated in polynomial time when $d$ is fixed.

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arXiv2024

Gaining or losing perspective for convex multivariate functions on box domains

MINLO (mixed-integer nonlinear optimization) formulations of the disjunction between the origin and a polytope via a binary indicator variable is broadly used in nonlinear combinatorial optimization for modeling a fixed cost associated with carrying out a group of activities and a convex cost function associated with the levels of the activities. The perspective relaxation of such models is often used to solve to global optimality in a branch-and-bound context, but it typically requires suitable conic solvers and is not compatible with general-purpose NLP software in the presence of other classes of constraints. This motivates the investigation of when simpler but weaker relaxations may be adequate. Comparing the volume (i.e., Lebesgue measure) of the relaxations as a measure of tightness, we lift some of the results related to the simplex case to the box case. In order to compare the volumes of different relaxations in the box case, it is necessary to find an appropriate concave upper bound that preserves the convexity and is minimal, which is more difficult than in the simplex case. To address the challenge beyond the simplex case, the triangulation approach is used.

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arXiv2024

On the Diameter of a 2-Sum of Polyhedra

The study of the combinatorial diameter of a polyhedron is a classical topic in linear-programming theory due to its close connection with the possibility of a polynomial simplex-method pivot rule. The 2-sum operation is a classical operation for graphs, matrices, and matroids; we extend this definition to polyhedra. We analyze the diameters of 2-sum polyhedra, which are those polyhedra that arise from this operation. These polyhedra appear in matroid and integer-programming theory as a natural way to link two systems in a joint model with a single shared constraint and the 2-sum also appears as a key operation in Seymour's decomposition theorem for totally-unimodular matrices. We show that the diameter of a 2-sum polyhedron is quadratic in the diameters of its summands. The methods transfer to a linear bound for the addition of a unit column to an equality system, or equivalently, to the relaxation of an equality constraint to an inequality constraint. Further, we use our methods to analyze the distance between vertices on certain faces of a 3-sum polyhedron.

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arXiv2024

On the Hardness of Short and Sign-Compatible Circuit Walks

The circuits of a polyhedron are a superset of its edge directions. Circuit walks, a sequence of steps along circuits, generalize edge walks and are "short" if they have few steps or small total length. Both interpretations of short are relevant to the theory and application of linear programming. We study the hardness of several problems relating to the construction of short circuit walks. We establish that for a pair of vertices of a $0/1$-network-flow polytope, it is NP-complete to determine the length of a shortest circuit walk, even if we add the requirement that the walk must be sign-compatible. Our results also imply that determining the minimal number of circuits needed for a sign-compatible decomposition is NP-complete. Further, we show that it is NP-complete to determine the smallest total length (for $p$-norms $\lVert \cdot \rVert_p$, $1 < p \leq \infty$) of a circuit walk between a pair of vertices. One method to construct a short circuit walk is to pick up a correct facet at each step, which generalizes a non-revisiting walk. We prove that it is NP-complete to determine if there is a circuit direction that picks up a correct facet; in contrast, this problem can be solved in polynomial time for TU polyhedra.

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arXiv2024

Generalized Scaling for the Constrained Maximum-Entropy Sampling Problem

The best practical techniques for exact solution of instances of the constrained maximum-entropy sampling problem, a discrete-optimization problem arising in the design of experiments, are via a branch-and-bound framework, working with a variety of concave continuous relaxations of the objective function. A standard and computationally-important bound-enhancement technique in this context is (ordinary) scaling, via a single positive parameter. Scaling adjusts the shape of continuous relaxations to reduce the gaps between the upper bounds and the optimal value. We extend this technique to generalized scaling, employing a positive vector of parameters, which allows much more flexibility and thus potentially reduces the gaps further. We give mathematical results aimed at supporting algorithmic methods for computing optimal generalized scalings, and we give computational results demonstrating the performance of generalized scaling on benchmark problem instances.

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arXiv2023

On computing sparse generalized inverses

The well-known M-P (Moore-Penrose) pseudoinverse is used in several linear-algebra applications; for example, to compute least-squares solutions of inconsistent systems of linear equations. It is uniquely characterized by four properties, but not all of them need to be satisfied for some applications. For computational reasons, it is convenient then, to construct sparse block-structured matrices satisfying relevant properties of the M-P pseudoinverse for specific applications. (Vector) 1-norm minimization has been used to induce sparsity in this context. Aiming at row-sparse generalized inverses motivated by the least-squares application, we consider 2,1-norm minimization (and generalizations). In particular, we show that a 2,1-norm minimizing generalized inverse satisfies two additional M-P pseudoinverse properties, including the one needed for computing least-squares solutions. We present mathematical-optimization formulations related to finding row-sparse generalized inverses that can be solved very efficiently, and compare their solutions numerically to generalized inverses constructed by other methodologies, also aiming at sparsity and row-sparse structure.

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arXiv2023

Computing D-Optimal solutions for huge-scale linear and quadratic response-surface models

We consider algorithmic approaches to the D-optimality problem for cases where the input design matrix is large and highly structured, in particular implicitly specified as a full quadratic or linear response-surface model in several levels of several factors. Using row generation techniques of mathematical optimization, in the context of discrete local-search and continuous relaxation aimed at branch-and-bound solution, we are able to design practical algorithms.

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arXiv2023

On the Combinatorial Diameters of Parallel and Series Connections

The investigation of combinatorial diameters of polyhedra is a classical topic in linear programming due to its connection with the possibility of an efficient pivot rule for the simplex method. We are interested in the diameters of polyhedra formed from the so-called parallel or series connection of oriented matroids: oriented matroids are the natural way to connect representable matroid theory with the combinatorics of linear programming, and these connections are fundamental operations for the construction of more complicated matroids from elementary matroid blocks. We prove that, for polyhedra whose combinatorial diameter satisfies the Hirsch-conjecture bound regardless of the right-hand sides in a standard-form description, the diameters of their parallel or series connections remain small in the Hirsch-conjecture bound. These results are a substantial step toward devising a diameter bound for all polyhedra defined through totally-unimodular matrices based on Seymour's famous decomposition theorem. Our proof techniques and results exhibit a number of interesting features. While the parallel connection leads to a bound that adds just a constant, for the series connection one has to linearly take into account the maximal value in a specific coordinate of any vertex. Our proofs also require a careful treatment of non-revisiting edge walks in degenerate polyhedra, as well as the construction of edge walks that may take a `detour' to facets that satisfy the non-revisiting conjecture when the underlying polyhedron may not.

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arXiv2023

Branch-and-bound for D-Optimality with fast local search and variable-bound tightening

We apply a branch-and-bound (B\&B) algorithm to the D-optimality problem based on a convex mixed-integer nonlinear formulation. We discuss possible methodologies to accelerate the convergence of the B\&B algorithm, by combining the use of different upper bounds, variable-bound tightening inequalities, and local-search procedures. Different methodologies to compute the determinant of a matrix after a rank-one update are investigated to accelerate the local-searches. We discuss our findings through numerical experiments with randomly generated test problem.

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arXiv2023

Generalized Scaling for the Constrained Maximum-Entropy Sampling Problem

The best techniques for the constrained maximum-entropy sampling problem, a discrete-optimization problem arising in the design of experiments, are via a variety of concave continuous relaxations of the objective function. A standard bound-enhancement technique in this context is scaling. We extend this technique to generalized scaling, we give mathematical results aimed at supporting algorithmic methods for computing optimal generalized scalings, and we give computational results demonstrating the usefulness of generalized scaling on benchmark problem instances.

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arXiv2023

Tridiagonal Maximum-Entropy Sampling and Tridiagonal Masks

The NP-hard maximum-entropy sampling problem (MESP) seeks a maximum (log-)determinant principal submatrix, of a given order, from an input covariance matrix $C$. We give an efficient dynamic-programming algorithm for MESP when $C$ (or its inverse) is tridiagonal and generalize it to the situation where the support graph of $C$ (or its inverse) is a spider graph with a constant number of legs (and beyond). We give a class of arrowhead covariance matrices $C$ for which a natural greedy algorithm solves MESP. A \emph{mask} $M$ for MESP is a correlation matrix with which we pre-process $C$, by taking the Hadamard product $M\circ C$. Upper bounds on MESP with $M\circ C$ give upper bounds on MESP with $C$. Most upper-bounding methods are much faster to apply, when the input matrix is tridiagonal, so we consider tridiagonal masks $M$ (which yield tridiagonal $M\circ C$). We make a detailed analysis of such tridiagonal masks, and develop a combinatorial local-search based upper-bounding method that takes advantage of fast computations on tridiagonal matrices.

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arXiv2023

Gaining or Losing Perspective for Convex Multivariate Functions on a Simplex

MINLO (mixed-integer nonlinear optimization) formulations of the disjunction between the origin and a polytope via a binary indicator variable have broad applicability in nonlinear combinatorial optimization, for modeling a fixed cost $c$ associated with carrying out a set of $d$ activities and a convex variable cost function $f$ associated with the levels of the activities. The perspective relaxation is often used to solve such models to optimality in a branch-and-bound context, especially in the context in which $f$ is univariate (e.g., in Markowitz-style portfolio optimization). But such a relaxation typically requires conic solvers and are typically not compatible with general-purpose NLP software which can accommodate additional classes of constraints. This motivates the study of weaker relaxations to investigate when simpler relaxations may be adequate. Comparing the volume (i.e., Lebesgue measure) of the relaxations as means of comparing them, we lift some of the results related to univariate functions $f$ to the multivariate case. Along the way, we survey, connect and extend relevant results on integration over a simplex, some of which we concretely employ, and others of which can be used for further exploration on our main subject.

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