The NEOS Server

Czyzyk, Joseph; Mesnier, Michael P.; Moré, Jorge J.

doi:10.1109/99.714603

Cited by 550 publications

(283 citation statements)

References 4 publications

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“…This objective function is extensively used through the literature and serves as a reference. All numerical results are obtained from the MINLP solver [1 5), available through the NEOS server on the Internet [16]. In all cases, solutions are obtained with E = 0.0001, and the flow limit for all commodities is bq = 2.5 Gbps (i.e.…”

Section: Resultsmentioning

confidence: 99%

A Tabu Search Heuristic for the Offline MPLS Reduced Complexity Layout Design Problem

Beker

Puech

Friderikos

2004

Lecture Notes in Computer Science

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Abstract. MPLS paths can be calculated on-line as demands arrive or off-line for an estimate of the traffic demand over time. Off-line calculation has the advantage of allowing a globally optimal network design. From the operational standpoint, a Iayout design for !arge networks should consider minimizing the Iayout complexity, thus reducing the cost of operation. In this paper, we formulate an optimization problern whose objective is to minimize the number of required hops. An end-to-end path delay constraint provides quality of service (QoS) guarantees. The resulting Minimum Path Set and Flow Allocation Problem (MPSFAP) , formulated as a Mixed Integer Non Linear Program (MINLP) is NP-complete. As such, it is only possible to solve it exactly on small size networks. To overcome this Iimitation, we designed a Tabu Search (TS) based algorithm that computes good quality approximate solutions for the MPSFAP problem. Salutions for medium to !arge size networks in reasonable CPU time and within acceptable optimality tolerances are found using the proposed TS based algorithm.

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Section: Resultsmentioning

confidence: 99%

A Tabu Search Heuristic for the Offline MPLS Reduced Complexity Layout Design Problem

Beker

Puech

Friderikos

2004

Lecture Notes in Computer Science

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“…Current optimization software is hobbled by its reliance on a plethora of input formats, as can be seen by even a cursory look at the list of solvers available on the NEOS Server ( [5,6] or www-neos.mcs.anl.gov/neos). The nearly 50 solvers in the NEOS lineup require instance inputs of about a dozen different kinds, including MPS [16] and LP formats for linear and integer programming, SMPS extensions to the MPS format for stochastic programming, formats such as SDPA specific to semidefinite programming, DIMACS min-cost flow and other formats for network linear programming, and proprietary formats used by two modeling language processors.…”

Section: Introductionmentioning

confidence: 99%

OSiL: An instance language for optimization

Fourer

Martin

2008

Comput Optim Appl

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Distributed computing technologies such as Web Services are growing rapidly in importance in today's computing environment. In the area of mathematical optimization, it is becoming increasingly common to separate modeling languages from optimization solvers.In fact, the modeling language software, solver software, and data used to generate a model instance might reside on different machines using different operating systems. Such a distributed environment makes it critical to have an open standard for exchanging model instances.In this paper we present OSiL (Optimization Services instance Language), an XMLbased computer language for representing instances of large-scale optimization problems including linear programs, mixed-integer programs, quadratic programs, and very general nonlinear programs. OSiL has two key features that make it much superior to current standard forms for optimization problem instances. First, it uses the object-oriented features of XML schemas to efficiently represent nonlinear expressions. Second, its XML schema maps directly into a corresponding in-memory representation of a problem instance. The inmemory representation provides a robust application program interface for general nonlinear programming, facilitates reading and writing postfix, prefix, and infix formats to and from the nonlinear expression tree, and makes the expression tree readily available for function and derivative evaluations.

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“…For the purpose of creating our figures we calculated a simple step function approximation of the arrival rate by dividing time into 64 equal intervals and in each of them estimating λ by (number of arrivals/length of interval). The optimization models were written in AMPL and solved by KNITRO (see [1] and [9]) on the NEOS servers (see [2], [3] and [6]). …”

Section: Results On a Real Data Setmentioning

confidence: 99%

Arrival Rate Approximation by Nonnegative Cubic Splines

Alizadeh

Eckstein

Noyan

et al.

2005 IEEE International Conference on Electro Information Technology

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Estimating the arrival rate function of a non-homogeneous Poisson process based on observed arrival data is a problem naturally arising in many applications. Cubic spline functions are particularly well-suited to represent such estimates since they are sufficiently versatile and easy to handle computationally. In this paper we present an optimization model to obtain cubic spline estimations based on the maximum likelihood principle. An important feature of any such model is that in order for us to be able to interpret the obtained splines as arrival rate functions they have to be nonnegative over the time interval of interest. We ensure this using a result of Nesterov characterizing nonnegative polynomials via positive semidefinite matrices. We also describe versions of our model allowing for periodic arrival rate functions and input data of limited precision. Numerical results based both on real-life and artificially generated data sets are also presented.

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The NEOS Server

Cited by 550 publications

References 4 publications

A Tabu Search Heuristic for the Offline MPLS Reduced Complexity Layout Design Problem

A Tabu Search Heuristic for the Offline MPLS Reduced Complexity Layout Design Problem

OSiL: An instance language for optimization

Arrival Rate Approximation by Nonnegative Cubic Splines

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