Instead, you will need to look for a solution or solutions that achieve an acceptable tradeoff between objectives. Deciding what tradeoffs are "acceptable" is a topic of investigation in its own right. There are a few free software packages specifically for multiple objective linear programming, including: ADBASE computes all efficient i. It is available without charge for research and instructional purposes.
If someone has a genuine need for such a code, they should send a request to: Ralph E. Other approaches that have worked are: Goal Programming treat the objectives as constraints with costed slacks , or, almost equivalently, form a composite function from the given objective functions; Pareto preference analysis essentially brute force examination of all vertices ; Put your objective functions in priority order, optimize on one objective, then change it to a constraint fixed at the optimal value perhaps subject to a small tolerance , and repeat with the next function. There is a section on this whole topic in [Nemhauser].
As a final piece of advice, if you can cast your model in terms of physical realities, or dollars and cents, sometimes the multiple objectives disappear! The commercial code OSL has features to assist in decomposing so-called Dantzig-Wolfe and staircase structures.
With any other code, you'll have to create your own decomposition framework and then call an LP solver to handle the subproblems. The folklore is that generally decomposition schemes take a long time to converge, so that they're slower than just solving the model as a whole -- although research continues. For now my advice, unless you are using OSL or your model is so huge that you can't buy enough memory to hold it, is to not bother decomposing it.
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It's probably more cost effective to upgrade your solver than to invest more time in programming a good piece of advice in many situations. It includes codes for convex hull computation, as well as for the opposite problem of generating all extreme points and extreme rays of a general convex polyhedron given by a system of linear inequalities. Here are further comments on some of these codes: Ken Clarkson has written Hull , an ANSI C program that computes the convex hull of a point set in general dimension.
The input is a list of points, and the output is a list of facets of the convex hull of the points, each facet presented as a list of its vertices. Qhull computes convex hulls as well as Delaunay triangulations, halfspace intersections about a point, Voronoi diagrams, and related objects. It uses the "Beneath Beyond" method, described in [Edelsbrunner].
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Komei Fukuda's cdd solves both the convex hull and vertex enumeration problems, using the Double Description Method of Motzkin et al. VE , another implementation of this approach by Fukuda and Mizukoshi, is available in a Mathematica implementation. See also the directory of computational geometry software compiled by Nina Amenta; and the archive of the former University of Minnesota Geometry Center website, which includes links to downloadable software.
Other algorithms for such problems are described in [Swart] , [Seidel] , and [Avis]. Such topics are said to be discussed in [Schrijver] page , [Chvatal] chapter 18 , [Balinski] , and [Mattheis] as well. Dash's Xpress-Parallel includes a branch-and-bound mixed-integer programming code designed to exploit both multi-processor computers and networks of workstations.
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OOPS is an object-oriented parallel implementation of the interior point algorithm, developed by Jacek Gondzio gondzio maths. The code can exploit any special structure of the problem. It runs on all parallel computing platforms that support MPI. Two parallel branch-cut-price frameworks are available to those who want to program specialized solvers for hard combinatorial problems that can be approached via integer programming: Symphony requires the user to supply model-specific preprocessing and separation functions, while other components including search tree, cut pool, and communication management are handled internally.
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Source code is included for basic applications to traveling salesman and vehicle routing problems. The distributed version runs in any environment supported by the PVM message passing protocol, and can also be compiled for shared-memory architectures using any OpenMP compliant compiler. Performance evaluations of parallel solvers must be interpreted with care.
One common measurement is the "speedup" defined as the time for solution using a single processor divided by the time using multiple processors.
A speedup close to the number of processors is ideal in some sense, but it is only a relative measure. The greatest speedups tend to be achieved by the least efficient codes, and especially by those that fail to take advantage of the sparsity predominance of zero coefficients in the constraints. For problems having thousands of constraints, a sparse single-processor code will tend to be faster than a non-sparse multiprocessor code running on current-day hardware. A network for this problem is viewed as a collection of nodes or circles or locations and arcs or lines or routes connecting selected pairs of nodes.
Arcs carry a physical or conceptual flow of some kind, and may be directed one-way or undirected two-way. Some nodes may be sources permitting flow to enter the network or sinks permitting flow to leave. This is a special case of the general linear programming problem. The transportation problem is an even more special case in which the network is bipartite: all arcs run from nodes in one subset to the nodes in a disjoint subset.
A variety of other well-known network problems, including shortest path problems, maximum flow problems, and certain assignment problems, can also be modeled and solved as network linear programs. Details are presented in many books on linear programming and operations research. Network linear programs can be solved 10 to times faster than general linear programs of the same size, by use of specialized optimization algorithms. Some commercial LP solvers include a version of the network simplex method for this purpose.
That method has the nice property that, if it is given integer flow data, it will return optimal flows that are integral. Integer network LPs can thus be solved efficiently without resort to complex integer programming software. Unfortunately, many different network problems of practical interest do not have a formulation as a network LP. These include network LPs with additional linear "side constraints" such as multicommodity flow problems as well as problems of network routing and design that have completely different kinds of constraints.
In principle, nearly all of these network problems can be modeled as integer programs. Some "easy" cases can be solved much more efficiently by specialized network algorithms, however, while other "hard" ones are so difficult that they require specialized methods that may or may not involve some integer programming.
Contrary to many people's intuition, the statement of a hard problem may be only marginally more complicated than the statement of some easy problem. A canonical example of a hard network problem is the " traveling salesman " problem of finding a shortest tour through a network that visits each node once. A canonical easy problem not obviously equivalent to a linear program is the "minimum spanning tree" problem to find a least-cost collection of arcs that connect all the nodes.
But if instead you want to connect only some given subset of nodes the "Steiner tree" problem then you are faced with a hard problem. These and many other network problems are described in some of the references below. Software for network optimization is thus in a much more fragmented state than is general-purpose software for linear programming.
The following are some of the implementations that are available for downloading. Most are freely available for many purposes, but check their web pages or "readme" files for details. It is a hard NP-complete problem just like integer programming , but the obvious integer programming formulations of it are not especially useful in getting good solutions within a reasonable amount of time.
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The TSP has attracted many of the best minds in the optimization field, and serves as a kind of test-bed for methods subsequently applied to more complex and practical problems. Methods have been explored both to give proved optimal solutions, and to give approximate but "good" solutions, with a number of codes being developed as a result: Concorde has solved a number of the largest TSPs for which proved optimal solutions are known. It employs a polyhedral approach, which is to say that it relies on a range of exceedingly sophisticated linear programming constraints, in a framework that resembles integer programming branch-and-bound methods.
The constraints are selectively generated as the solution process proceeds. The full C code is available without cost for research purposes. Public domain code for the Asymmetric TSP with travel between two cities significantly cheaper in one of the two directions is available in TOMS routine , documented in [Carpaneto]. Code for a solver can be obtained via instructions in [Volgenant]. Chad Hurwitz churritz cts. Numerical Recipes [Press] contains code that uses simulated annealing.
Stephan Mertens's TSP Algorithms in Action uses Java applets to illustrate some simple heursitics and compare them to optimal solutions, on node problems. Onno Waalewijn has constructed Java TSP applets exhibiting the behavior of different methods for heuristic and exhaustive search on various test problems.
Related Interior Point Techniques in Optimization: Complementarity, Sensitivity and Algorithms
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