Guided construction search metaheuristics for the capacitated p-median problem with single source constraint

In the capacitated p-median problem with single source constraint, also known as the capacitated clustering problem, a given set of n weighted points is to be partitioned into p clusters such that the total weight of the points in each cluster does not exceed a given cluster capacity. The objective is to find a set of p centres that minimizes the total scatter of points allocated to these clusters. In this paper, a (λ, μ)-interchange neighbourhood based on the concept of λ-interchange of points restricted to μ-adjacent clusters is proposed. Structural properties of centres are identified and exploited to derive special data structures for their efficient evaluations. Different search and selection strategies including the variable neighbourhood search descent with respect to μ-nearest points are investigated. The most efficient strategies are then embedded in a guided construction search metaheuristic framework based either on a periodic local search procedure or a greedy random adaptive search procedure to solve the problem. Computational experience is reported on a standard set of benchmarks. The computational experience demonstrates the competitive performance of the proposed algorithms when compared to the best-known procedures in the literature in terms of solution quality and computational requirement.     

Analysis of Local Search Landscapes for <Emphasis Type="Italic">k</Emphasis>-SAT Instances

Stochastic local search is a successful technique in diverse areas of combinatorial optimisation and is predominantly applied to hard problems. When dealing with individual instances of hard problems, gathering information about specific properties of instances in a pre-processing phase is helpful for an appropriate parameter adjustment of local search-based procedures. In the present paper, we address parameter estimations in the context of landscapes induced by k-SAT instances: at first, we utilise a sampling method devised by Garnier and Kallel in 2002 for approximations of the number of local maxima in landscapes generated by individual k-SAT instances and a simple neighbourhood relation. The objective function is given by the number of satisfied clauses. The procedure provides good approximations of the actual number of local maxima, with a deviation typically around 10%. Secondly, we provide a method for obtaining upper bounds for the average number of local maxima in k-SAT instances. The method allows us to obtain the upper bound \(2^{n-O(\sqrt{n/k})}\) for the average number of local maxima, if m is in the region of 2 ^k · n/k.     

A combined local search and integer programming approach to the traveling tournament problem

The traveling tournament problem is a well-known combinatorial optimization problem with direct applications to sport leagues scheduling, that sparked intensive algorithmic research over the last decade. With the Challenge Traveling Tournament Instances as an established benchmark, the most successful approaches to the problem use meta-heuristics like tabu search or simulated annealing, partially heavily parallelized. Integer programming based methods on the other hand are hardly able to tackle larger benchmark instances. In this work we present a hybrid approach that draws on the power of commercial integer programming solvers as well as the speed of local search heuristics. Our proposed method feeds the solution of one algorithm phase to the other one, until no further improvements can be made. The applicability of this method is demonstrated experimentally on the galaxy instance set, resulting in currently best known solutions for most of the considered instances.     

Tabu search embedded simulated annealing for the shortest route cut and fill problem

The shortest route cut and fill problem proposed by Henderson et al ¹ is studied in this paper where we extend the model to include multiple vehicles and a makespan objective. A new tabu search embedded simulated annealing algorithm for both models is developed. Computational experiments show that the new approach is robust and achieves better solutions when compared with those found using Henderson et al's algorithm for larger test cases within significantly shorter times.     

Normal forms of functional-differential equations of Beardon type and Briot–Bouquet type equations in the complex domain

We study local analytic solutions of the functional-differential equation of the form \({h(\psi(z)) = b(z) h(z) h^\prime(z) + d(z)h(z)^{2}}\) which are called Beardon type functional-differential equations. All functions involved are supposed to be holomorphic in a neighbourhood of zero. Special cases are the equations f(kz) =  kf(z) f′(z) where k is a complex number, \({k \neq 0}\), and \({f(\varphi(z)) = a(z) f(z) f'(z)}\) with given \({\varphi}\) and a. The class of these equations is invariant under transformations \({h \to \alpha h, \alpha(z) \neq 0}\) for all z in a neighbourhood of zero, of the unknown function and \({z \to T(z)}\) of the argument z. In particular, we are interested to know under which conditions a Beardon type functional-differential equation can be transformed to the simplified (normal form) \({h(kz) = k h(z) h'(z) + c(z) h(z)^2}\) where \({k \in \mathbb {C} \backslash\left\{0\right\}}\). We solve this normal form by another transfomation to a so-called Briot–Bouquet type functional-differential equation.     

Subgraph ejection chains and tabu search for the crew scheduling problem

The tabu search algorithms for the Crew Scheduling Problem (CSP) reported in this paper are part of a decision support system for crew scheduling management of the Lisbon Underground. The CPS is formulated as the minimum number of duties necessary to cover a pre-defined timetable under a set of contractual rules. An initial solution is constructed following a traditional run-cutting approach. Two alternative improvement algorithms are subsequently used to reduce the number of duties in the initial solution. Both algorithms are embedded in a tabu search framework: Tabu-crew takes advantage of a form of strategic oscillation for the neighbourhood search while the run-ejection algorithm considers compound moves based on a subgraph ejection chain method. Computational results are reported for a set of real problems.     

Heuristics for exact nonnegative matrix factorization

The exact nonnegative matrix factorization (exact NMF) problem is the following: given an m-by-n nonnegative matrix X and a factorization rank r, find, if possible, an m-by-r nonnegative matrix W and an r-by-n nonnegative matrix H such that \(X = WH\). In this paper, we propose two heuristics for exact NMF, one inspired from simulated annealing and the other from the greedy randomized adaptive search procedure. We show empirically that these two heuristics are able to compute exact nonnegative factorizations for several classes of nonnegative matrices (namely, linear Euclidean distance matrices, slack matrices, unique-disjointness matrices, and randomly generated matrices) and as such demonstrate their superiority over standard multi-start strategies. We also consider a hybridization between these two heuristics that allows us to combine the advantages of both methods. Finally, we discuss the use of these heuristics to gain insight on the behavior of the nonnegative rank, i.e., the minimum factorization rank such that an exact NMF exists. In particular, we disprove a conjecture on the nonnegative rank of a Kronecker product, propose a new upper bound on the extension complexity of generic n-gons and conjecture the exact value of (i) the extension complexity of regular n-gons and (ii) the nonnegative rank of a submatrix of the slack matrix of the correlation polytope.     

Efficiency of interactive multi-objective simulated annealing through a case study

We have previously developed an adaptation of the simulated annealing for Multi-Objective Combinatorial Optimisation (MOCO) problems to construct an approximation of the efficient set of such problems. Here we transform this approach to propose an interactive procedure deriving a ‘good compromise’. The method is tested on the problem of homogeneous grouping of nuclear fuel cans which is in fact multi-objective even if the approaches proposed in the literature do not handle this multi-dimensional characteristic.     

Stability and convergence of a fully discrete local discontinuous Galerkin method for multi-term time fractional diffusion equations

In this paper, a fully discrete local discontinuous Galerkin method for a class of multi-term time fractional diffusion equations is proposed and analyzed. Using local discontinuous Galerkin method in spatial direction and classical L1 approximation in temporal direction, a fully discrete scheme is established. By choosing the numerical flux carefully, we prove that the method is unconditionally stable and convergent with order O(h ^k+1 + (Δt)^2?α ), where k, h, and Δt are the degree of piecewise polynomial, the space, and time step sizes, respectively. Numerical examples are carried out to illustrate the effectiveness of the numerical scheme.     

Design of balanced MBA student teams

In some schools and universities, students must sometimes be divided into several teams in such a way that each team provides a good representation of the classroom population. In this paper, two different ways of measuring the balance among teams are proposed: min-sum and min-max objective functions. For the first function and the L₁-norm used in the space of attributes, an exact solution method based on a set partitioning formulation and on the enumeration of all possible team patterns is presented. For the second objective function, a set partitioning formulation is also considered, but as an approximation. In order to solve large problem instances, we have also developed metaheuristics based on variable neighbourhood search. Models and methods are tested on data from an MBA programme.     

A new infeasible-interior-point algorithm for linear programming over symmetric cones

In this paper we present an infeasible-interior-point algorithm, based on a new wide neighbourhood N(τ₁, τ₂, η), for linear programming over symmetric cones. We treat the classical Newton direction as the sum of two other directions. We prove that if these two directions are equipped with different and appropriate step sizes, then the new algorithm has a polynomial convergence for the commutative class of search directions. In particular, the complexity bound is O(r^1.5logε^?1) for the Nesterov-Todd (NT) direction, and O(r²logε^?1) for the xs and sx directions, where r is the rank of the associated Euclidean Jordan algebra and ε > 0 is the required precision. If starting with a feasible point (x⁰, y⁰, s⁰) in N(τ₁, τ₂, η), the complexity bound is \(O\left( {\sqrt r \log {\varepsilon ^{ - 1}}} \right)\) for the NT direction, and O(rlogε^?1) for the xs and sx directions. When the NT search direction is used, we get the best complexity bound of wide neighborhood interior-point algorithm for linear programming over symmetric cones.     

Hybridizations within a graph-based hyper-heuristic framework for university timetabling problems

A significant body of recent literature has explored various research directions in hyper-heuristics (which can be thought as heuristics to choose heuristics). In this paper, we extend our previous work to construct a unified graph-based hyper-heuristic (GHH) framework, under which a number of local search-based algorithms (as the high level heuristics) are studied to search upon sequences of low-level graph colouring heuristics. To gain an in-depth understanding on this new framework, we address some fundamental issues concerning neighbourhood structures and characteristics of the two search spaces (namely, the search spaces of the heuristics and the actual solutions). Furthermore, we investigate efficient hybridizations in GHH with local search methods and address issues concerning the exploration of the high-level search and the exploitation ability of the local search. These, to our knowledge, represent entirely novel directions in hyper-heuristics. The efficient hybrid GHH obtained competitive results compared with the best published results for both benchmark course and exam timetabling problems, demonstrating its efficiency and generality across different problem domains. Possible extensions upon this simple, yet general, GHH framework are also discussed.     

A mapping technique for better solution exploration: NSGA-II adaptation

A mapping method (MaM) for a better solution space exploration adapted to NSGA-II method is presented. The Mapping technique divides the solution space into several zones using a Hamming distance to a reference solution. We present a bijective mapping function from the search space to the binary representation space of solutions. For each zone, a mapping metric is used to evaluate the solution space exploration. According to this evaluation, a local search is performed. The mapping is adapted to the well known non-dominated sorting genetic algorithm-II (NSGA-II) method applied to solve the flexible job shop problem (FJSP) case. We present the comparison between the hybridization using the local search for the non-dominated solutions and the hybridization using the mapping metrics. The multi-objective metrics show the efficiency of mapping adaptation in terms of convergence and diversity.     

Analysis of a new finite difference/local discontinuous Galerkin method for the fractional Cattaneo equation

In this paper, we first present a new finite difference scheme to approximate the time fractional derivatives, which is defined in the sense of Caputo, and give a semidiscrete scheme in time with the truncation error O((Δt)^3?α ), where Δt is the time step size. Then a fully discrete scheme based on the semidiscrete scheme for the fractional Cattaneo equation in which the space direction is approximated by a local discontinuous Galerkin method is presented and analyzed. We prove that the method is unconditionally stable and convergent with order O(h ^k+1 + (Δt)^3?α ), where k is the degree of piecewise polynomial. Numerical examples are also given to confirm the theoretical analysis.     

Packing circles in the smallest circle: an adaptive hybrid algorithm

The circular packing problem (CPP) consists of packing n circles C _i of known radii r _i , i∈N={1,?…,?n}, into the smallest containing circle ?. The objective is to determine the coordinates (x _i ,?y _i ) of the centre of C _i , i∈N, as well as the radius r and centre (x,?y) of ?. CPP, which is a variant of the two-dimensional open-dimension problem, is NP hard. This paper presents an adaptive algorithm that incorporates nested partitioning within a tabu search and applies some diversification strategies to obtain a (near) global optimum. The tabu search is to identify the n circles’ ordering, whereas the nested partitioning is to determine the n circles’ positions that yield the smallest r. The computational results show the efficiency of the proposed algorithm.     

An exact method for minimizing the total treatment time in intensity-modulated radiotherapy

In this paper, we solve a combinatorial optimization problem that arises from the treatment planning of a type of radiotherapy where intensity is modulated by multileaf collimators (MLC) in a step-and-shoot manner. In Ernst et al [INFORMS Journal on Computing 21 (4) (2009): 562–574], we proposed an exact method for minimizing the number of MLC apertures needed for a treatment. Our method outperformed the fastest algorithms available at the time. We refer to our method as the CPI method. We now attempt to minimize the total treatment time by modifying our CPI method. This modification involves non-trivial work, as some of the search space elimination schemes used in the CPI method cannot be applied in here. In our numerical experiments, we again compare our new method with the fastest algorithms currently available. There has been significant recent research in this area; hence we compare our method with those published in Wake et al [Computers and Operations Research 36 (2009): 795–810], Ta?kin et al [Operations Research 58 (3) (2010): 674–690] and Cambazard et al [CPAIRO (2010): 1–16]. The numerical comparisons indicate that our method generally outperformed the first two, while being competitive with the third.     

On Generalized Derivations and Centralizers of Operator Algebras with Involution

Let B(H) be the algebra of all bounded linear operators on a complex Hilbert space H and A(H) ? B(H) be a standard operator algebra which is closed under the adjoint operation. Let F: A(H)→ B(H) be a linear mapping satisfying F(AA*A) = F(A)A*A + Ad(A*)A + AA*d(A) for all A ∈ A(H), where the associated linear mapping d: A(H) → B(H) satisfies the relation d(AA*A) = d(A)A*A + Ad(A*)A + AA*d(A) for all A ∈ A(H). Then F is of the form F(A) = SA ? AT for all A ∈ A(H) and some S, T ∈ B(H), that is, F is a generalized derivation. We also prove some results concerning centralizers on A(H) and semisimple H^*-algebras.     

Special issue: recent progress on iterative methods for large systems of equations

Let A x = b be a large and sparse system of linear equations where A is a nonsingular matrix. An approximate solution is frequently obtained by applying preconditioned iterations. Consider the matrix B = A + P Q ^T where \(P, Q \in \mathbb {R}^{n \times k}\) are full rank matrices. In this work, we study the problem of updating a previously computed preconditioner for A in order to solve the updated linear system B x = b by preconditioned iterations. In particular, we propose a method for updating a Balanced Incomplete Factorization preconditioner. The strategy is based on the computation of an approximate Inverse Sherman-Morrison decomposition for an equivalent augmented linear system. Approximation properties of the preconditioned matrix and an analysis of the computational cost of the algorithm are studied. Moreover, the results of the numerical experiments with different types of problems show that the proposed method contributes to accelerate the convergence.     

A Classification of Spectrum-determined Circulant Digraphs

Some researchers have proved that ádám’s conjecture is wrong. However, under special conditions, it is right. Let Z_n be a cyclic group of order n and C_n(S) be the circulant digraph of Z_n with respect to S ? Z_n\{0}. In the literature, some people have used a spectral method to solve the isomorphism for the circulants of prime-power order. In this paper, we also use the spectral method to characterize the circulants of order p^aq^bw^c(where p, q and w are all distinct primes), and to make ádám’s conjecture right.     

On the complexity of the gradient of a rational function

The Baur-Strassen method implies L(?f) ? 4L(f), where L(f) is the complexity of computing a rational function f by arithmetic circuits, and ?f is the gradient of f. We show that L(? f) ? 3L(f) + n, where n is the number of variables in f. In addition, the depth of a circuit for the gradient is estimated.