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Model business issues mathematically and solve them
with IBM ILOG CPLEX Optimizer\’s powerful algorithms
to produce precise and logical decisions.

* Improve efficiency, quickly implement strategies,
and increase profitability. IBM ILOG CPLEX
Optimizer\’s mathematical optimization technology
enables smarter decision-making for efficient
resource utilization.
* Robust algorithms for demanding problems: IBM ILOG
CPLEX Optimizer has solved problems with millions
of constraints and variables.
* Industry-leading support: IBM has an impressive
rate of product improvement and ample support
resources to serve you.
* High performance: IBM ILOG CPLEX Optimizer
delivers the power needed to solve very large,
real-world optimization problems, as well as the
speed required for today\’s interactive
* Fundamental algorithms: IBM ILOG CPLEX Optimizer
provides flexible, high-performance optimizers for
solving linear programming, quadratic programming,
quadratically constrained programming and mixed
integer programming problems.
* Robust and reliable: A large installed base helps
us make IBM ILOG CPLEX Optimizer better with each
release. Every new feature is tested on the
biggest, most diverse model library in the world.
* Flexible interfaces: IBM ILOG CPLEX Optimizer
gives developers a variety of ways to interact
with it during the development and deployment of
their applications.
* IBM ILOG CPLEX Optimizer is a component of IBM
ILOG CPLEX Optimization Studio, which combines and
simplifies IBM\’s product offerings for
optimization model development, solving, and
deployment. It offers in a single package, all the
functionality that was previously available among
an array of product and component configurations,
making all tools and technologies available during
prototyping and development.
* IBM ILOG CPLEX CP Optimizer provides a
complementary optimization technology based on
constraint programming that is especially suitable
for detailed scheduling and other difficult
combinatorial problems.
* Latest version offers faster MIP solutions,
especially on multiprocessor machines, improved
connectors to 3rd party modeling tools, new
graphical views in the IDE including Gantt charts
and cumulative functions, improved syntactical
support for detailed scheduling models, and
additional API support for constraint programming
search and propagation.

IBM ILOG CPLEX Optimizer features

Automatic and dynamic algorithm parameter control.
IBM ILOG CPLEX Optimizer automatically determines
\”smart\” settings for a wide range of algorithm
parameters, usually resulting in optimal linear
programming solution performance. However, for a
more hands-on approach, dozens of parameters may be
manually adjusted, including algorithmic strategy
controls, output information controls, optimization
duration limits, and numerical tolerances.

Fast, automatic restarts from an advanced basis.
Large problems can be modified, and then solved
again in a fraction of the original solution time.

A variety of problem modification options, such as:
* The ability to add and delete variables
* The ability to add and delete constraints
* The ability to modify objective, right-hand side,
bound and matrix coefficients
* The ability to change constraint types

A wide variety of input/output options, such as:
* Problem files: read/write MPS files, IBM ILOG
CPLEX Optimizer LP files, MPS basis and revise
files, binary problem/basis files
* Log files: session information and various
solution reports
* Solution files: ASCII and binary solution files
* IBM ILOG CPLEX Optimizer messages: Each message
type (such as RESULTS, WARNINGS or ERRORS) can be
directed to specified files, or completely

Post solution information and analysis, including:
* Objective function value
* Solution variable and slack values
* Constraint dual values (shadow prices)
* Variable reduced costs
* Right-hand side, objective function, and bound
sensitivity ranges
* Basic variables and constraints
* Solution infeasibilities (if any exist)
* Iteration/node count, solution time, process data
* Infeasibility (IIS) finder for diagnosing problem
Size:55 MB