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NEOS Server

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  • Comment: The raised issues have not been sufficiently addressed. Tvx1 21:31, 15 April 2016 (UTC)
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The NEOS Server is an internet-based client-server application that provides free access to a library of optimization solvers. Its library of solvers includes more than 60 commercial, free and open source solvers, which can be applied to mathematical optimization problems of more than 12 different types, including linear programming, integer programming and nonlinear optimization.

The server is managed by the Wisconsin Institute for Discovery at the University_of_Wisconsin-Madison. Most of the solvers are hosted by the University of Wisconsin in Madison, where jobs run on a cluster of high-performance machines managed by the HTCondor software]]. A smaller number of solvers are hosted by partner organizations: Argonne National Laboratory, Arizona State University, the University of Klagenfurt in Austria, and the University of Minho in Portugal. The server was developed in 1996 by the Optimization Technology Center of Argonne National Laboratory and Northwestern University.

Graphical depiction of the structure of the NEOS Server

Structure

The NEOS (Network-Enabled Optimization System) project was launched in at Argonne National Laboratory and Northwestern University to develop a method to share optimization software resources over the internet. [1][2][3] The server went live in 1996, one of the first examples of software as a service.

The NEOS Server is an internet-based client-server application that provides access to a library of optimization solvers. The server accepts optimization models described in modeling languages, programming languages, and problem-specific formats. Most of the linear programming, integer programming and nonlinear programming solvers accept input from AMPL and/or GAMS. Jobs can be submitted via a web page, email, XML_RPC, Kestrel[4] or indirectly via third party submission tools SolverStudio for Excel, OpenSolver, and Pyomo). NEOS uses the HTCondor software to manage the workload on a dedicated cluster of computers.[5]

Types of available solvers

The NEOS Server provides access to solvers in the following optimization categories:

  • Bound Constrained Optimization: solvers for nonlinear optimization problems that are constrained only by bounds on the variables
  • Combinatorial optimization: solvers for specific problems such as the Traveling Salesman Problem
  • Complementarity Problems: solvers for optimizing a function of two vector variables subject to constraint(s) including the requirement that the inner product of the two vectors must equal zero
  • Global optimization: solvers for optimizing a general nonlinear function with the goal of finding the global minimum or maximum value
  • Linear programming: solvers for optimizing a linear objective function subject to linear equality and linear inequality constraints
  • Mixed Integer Linear Programming: solvers for optimizing a linear program in which some or all of the variables are restricted to be integers
  • Mixed Integer Nonlinearly Constrained Optimization: solvers for nonlinear optimization problems in which at least some of the variables are restricted to discrete values
  • Nonlinearly Constrained Optimization: solvers for optimization problems in which some of the constraints and/or the objective function are nonlinear
  • Semidefinite programming: solvers for optimizing a linear objective function over the intersection of the cone of positive semidefinite matrices with an affine space
  • Stochastic linear programming: solvers for optimizing problems in which some of the data incorporated into the objective function or constraints is uncertain
  • Second order_cone programming: solvers for convex optimization problems that include second-order cone constraints
  • Semi-Infinite Optimization: solvers for optimization problems with a finite number of variables and an infinite number of constraints or with an infinite number of variables and a finite number of constraints
  • Unconstrained Optimization: solvers for optimizing a function whose feasible region is not constrained

References

  1. ^ Czyzyk, Joseph; Owen, Jonathan H.; Wright, Stephen J. (1997). "Optimization on the Internet". OR/MS Today. 24 (5): 48-51.
  2. ^ Czyzyk, Joseph; Mesnier, Michael P.; Moré, Jorge J. (1998). "The NEOS Server". IEEE Journal on Computational Science and Engineering. 5 (3): 68 - 75.
  3. ^ Dolan, Elizabeth D.; Fourer, Robert; Moré, Jorge J.; Munson, Todd S. (2002). "Optimization on the NEOS Server" (PDF). SIAM News. 35 (6): 8-9.
  4. ^ Dolan, Elizabeth D.; Fourer, Robert; Goux, Jean-Pierre; Munson, Todd S.; Sarich, Jason (2008). "Kestrel: An Interface from Optimization Modeling Systems to the NEOS Server" (PDF). INFORMS Journal on Computing. 20 (4): 525 - 538.
  5. ^ Ferris, Michael C.; Mesnier, Michael P.; Moré, Jorge J. (2000). "NEOS and Condor: Solving Nonlinear Optimization Problems over the Internet". ACM Transactions on Mathematical Software. 26: 1–18.
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