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ILOG CPLEX 11.0
- Mixed Integer Programming (MIP) Performance
ILOG CPLEX 11 introduces a new search algorithm—dynamic search. It is innovative in its integration and sequencing of the usual branching, nodes and cuts in branch-and-cut algorithms. ILOG CPLEX 11 retains its conventional branch-and-cut algorithm, but with advances in branching, cuts and heuristics. By selecting the more efficient of the two search strategies, ILOG CPLEX 11 improves the time to optimality by 15 percent on average for models solved in less than one minute; and it solves models in the range of one minute to one hour an average of three times faster. For hard models requiring more than one hour to solve, the speed up is a factor of ten on average.
- Enhanced Parallel MIP
ILOG CPLEX 11 extends the functionality of the parallel MIP optimizer to include two modes of operation. In deterministic mode, a newly implemented search algorithm exploits parallelism in solving nodes of the branch-and-cut tree, but produces a repeatable, invariant solution path. In opportunistic mode, the search algorithm (introduced in a previous release), takes full advantage of parallelism; it performs less synchronization between threads and allows random tie breaking, which may result in different solution paths but potentially faster performance.
- Multiple MIP Solutions
ILOG CPLEX 11 introduces the solution pool feature, which allows users to consider multiple solutions to a MIP model. In practice, a single—even optimal—solution is not always sufficient, because every aspect of a problem cannot always be perfectly captured in a MIP model. The solution pool feature offers a mechanism for exploring the effects of subjective preferences on the solution space without enforcing them as constraints in the model. ILOG CPLEX 11 can collect all (optimal) solutions or solutions that satisfy specific criteria. Solution pool parameters and filters allow users to control the properties of the solutions generated and stored in the solution pool.
- Performance Tuning
ILOG CPLEX 11 introduces a performance tuning utility to help users improve the performance of their optimization applications. It analyzes one model or a group of models to identify parameter settings that yield better performance than default settings. ILOG CPLEX 11 tries different parameter settings based on the outcome of the initial and subsequent model runs. Users can customize the tuning and can apply it to models that are solved to optimality or to earlier stopping criteria.
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ILOG CPLEX 10.0
- Performance
ILOG CPLEX 10.0 provides several performance improvements. For a set of difficult LP problems, ILOG CPLEX 10.0 has improved the time to optimality by an average of 20% with both the Primal Simplex algorithm and the Barrier Optimizer. For MILP models, ILOG CPLEX 10.0 has improved the time to optimality, on average, by 35 percent overall, and by 70 percent for particularly difficult models.
- Infeasibility Analysis
Given an infeasible model, the Conflict Refiner can identify contradictory constraints and bounds within the model to help users identify the causes of the infeasibility. The Conflict Refiner can work on any type of problem, even mixed integer programs and those containing quadratic elements. The Conflict Refiner also uses groups and preferences to allow the user to guide the process. Model types, groups and preferences make the Conflict Refiner an extension and generalization of the IIS finder.
- Solution Polishing
Solution Polishing is a new ILOG CPLEX heuristic used to boost performance on certain types of models. Solution Polishing is appropriate for finding the best solutions to complex and difficult MIP models within a specified time. Solution Polishing is used to improve the best solution at the end of the branch-and-cut process if optimality has not been proven. It can also be used instead of the branch-and-cut algorithm if an initial solution can be found in the root node.
- Indicators
Indicators are new constraint types that allow users to express relationships among variables by identifying a binary variable to control whether or not a specified linear constraint is active. Formulations using indicator constraints are more numerically robust and accurate than conventional formulations involving so-called Big M data. In Callable Library applications, ILOG CPLEX supports the direct use of indicator constraints in the model. In Concert Technology applications, ILOG CPLEX 10.0 automatically uses indicator constraints when it encounters expressions that can be linearized, such as “IloIfThen.”
- MIP Starts
The advanced restart capabilities of ILOG CPLEX have been improved to utilize initial solutions, partial solutions and partially correct solutions. ILOG CPLEX will accept a complete and valid solution, which can significantly improve the time it takes to find an optimal solution. Users can specify values for a subset of the discrete variables and ILOG CPLEX 10.0 will attempt to fill in the missing values or correct the wrong values in a way that leads to an integer-feasible solution, potentially reducing the time to solve the problem.
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ILOG CPLEX 9.0
- Performance improvements
ILOG CPLEX 9.0 contains major enhancements that improve performance for mixed-integer
and linear programs. The ILOG CPLEX MIP Optimizer is 50% faster, on average,
for a set of difficult customer models. ILOG CPLEX Simplex Optimizers have also
improved. The dual simplex algorithm is 40% faster and the primal simplex
is 20% faster, on average. (Performance improvements are measured using
the default setting of current and previous ILOG CPLEX versions.)
- Concert Technology for .NET
Users
ILOG Concert Technology for .Net Users is a new application programming interface
(API). This API gives developers the ability to write mathematical programming
applications using any .NET-supported languages. ILOG CPLEX includes examples
in C# and Visual Basic .NET. Programming objects make it easy to define variables,
define constraints and interact with models. Users can create models, modify
models or customize ILOG CPLEX algorithms, all without leaving their favorite
.NET programming language.
- Quadratically constrained programs
Quadratically constrained programs (QCPs) can be solved using ILOG CPLEX. In QCPs,
quadratic terms may appear in one or more of the problem’s constraints.
Such a problem’s objective function may or may not contain quadratic terms
as well. The ILOG CPLEX Mixed Integer Optimizer has been extended to handle
QCPs with integrality constraints. The full complement of MIP features
-- including presolve, probing, cuts, heuristics, and callbacks -- is available
for solving MIQCP problems. A MIQCP problem may contain any combination of continuous,
binary, general integer, semi-continuous or special ordered set variables.
- Infeasibility analysis tool
ILOG CPLEX provides an automatic approach to find the best feasible alternative
to an infeasible model. This approach is called FeasOpt (for feasible optimization).
FeasOpt accepts an infeasible model and selectively relaxes the bounds
and constraints, minimizing a weighted penalty function that you define.
In essence, FeasOpt tries to suggest the smallest change that would achieve
feasibility. FeasOpt does not actually modify your model. Instead, it returns
a suggested set of bounds and constraint ranges, along with the solution
that would result from these relaxations. Optionally, FeasOpt can provide
an optimal solution to the original objective function with the new relaxed
model.
- XML
ILOG Concert Technology for C++ Users provides an API that allows you to
serialize models and solutions in XML. An XML schema specifies the format
of optimization models in XML. Users can serialize custom model objects, in addition to using ready-made ILOG objects. Models and solutions can
be read into Concert.
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The ILOG Optimization Suite |
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ILOG Optimization Technologies Workshop |
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29 May 2008
Pittsburgh, PA |
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