Cplex Free Trial

[Kevin]

Yeswe offer IBM ILOG CPLEX Optimization no-cost edition. You can register to download it at the link below. The no-cost edition is restricted to problems up to 1,000 variables and 1,000 constraints. A time-limited trial without restrictions on problem size is available upon request by contacting an IBM sales representative.

Beyond the commercial version, IBM runs the IBM Academic Initiative where students and faculty members have access to the product at no cost. Our academic entry is without functional or model size limitations, and you must register to check your eligibility. If the program accepts you, then you will get an email notification with download instructions.

The CPLEX subscription can be purchased as a monthly or annual subscription and is charged at the beginning of the billing period. You will be automatically billed on a periodic basis, according to the terms of your subscription.

A single subscription may include one or more users, and each user receives a personal key to unlock the software. Each member of your team who uses IBM ILOG CPLEX Optimization Studio needs to have his/her own key. Users can be managed from My Products and Services. When you remove a user from the subscription, his or her key becomes inactive. When you add a user to the subscription, he or she receives a key.

Each day when the first "solve" happens, the software goes to check eligibility to use the product unlimited. However, after at least one check, the user can be offline for 14 days before the product goes back to the limited mode. If the user connects and eligibility is checked before the end of the 14th day, then the user again could be offline for another 14 days.

The IBM ILOG CPLEX Optimization Studio subscription is supported on Windows, macOS, and Linux (on x86 platforms). Please refer to the system requirements information on the supported releases of this product.

I'm using MATLAB 2010b 64bit and its cplex integration to solve an engineering problem. However, because of the memory leak of cplex, memory usage exceeds acceptable limits with cplex (100+GBs including virtual memory) hence I am not able to solve my problem. You can see a similar post here.

Then I tried to use MATLAB linprog from the optimization toolbox but the result was disappointing. The running time of the algorithm for a small problem instance was increased from 80 cpu sec to 2600 cpu sec.

I would be very surprised if there was a memory leak in cplex. If you have a large problem then the memory will grow with any sensible solver. Is there perhaps a memory leak in the interface to cplex? How big is your problem? Are you running multi threaded as each thread will take a copy of the problem and hence will eat a lot more memory.

After some trials to fix MATLAB/CPLex API's memory usage problem (memory leak) and after referring to some studies I decided to switch to Gurobi solver. For pure LP problems, it seems to be slightly slower compared to CPlex but this can be due to the way I use Gurobi. Someone may find Gurobi faster compared to CPlex. I suggested that on my previous posts under different questions. Here are some academic studies[Analysis of commercial and free and open source solvers for linear optimization problems][1]

TOMLAB /CPLEX efficiently integrates the solver package CPLEX with Matlab and TOMLAB. The solver is generally considered the state-of-the-art large-scale mixed-integer linear and quadratic programming solver.The package includes simplex and barrier solvers for linear, quadratic and conic programming.Included is also an advanced Matlab solution for network programming problems.A full memory trial version ofTOMLAB /CPLEX is available for download.

GAMS/Cplex is a GAMS solver that allows users to combine the high level modeling capabilities of GAMS with the power of Cplex optimizers. Cplex optimizers are designed to solve large, difficult problems quickly and with minimal user intervention. Access is provided (subject to proper licensing) to Cplex solution algorithms for linear, quadratically constrained and mixed integer programming problems. While numerous solving options are available, GAMS/Cplex automatically calculates and sets most options at the best values for specific problems.

Cplex solves LP problems using several alternative algorithms. The majority of LP problems solve best using Cplex's state of the art dual simplex algorithm. Certain types of problems benefit from using the primal simplex algorithm, the network optimizer, the barrier algorithm, or the sifting algorithm. The concurrent option will allow solving with different algorithms in parallel. The solution is returned by the first to finish.

Solving linear programming problems is memory intensive. Even though Cplex manages memory very efficiently, insufficient physical memory is one of the most common problems when running large LPs. When memory is limited, Cplex will automatically make adjustments which may negatively impact performance. If you are working with large models, study the section entitled Physical Memory Limitations carefully.

Cplex is designed to solve the majority of LP problems using default option settings. These settings usually provide the best overall problem optimization speed and reliability. However, there are occasionally reasons for changing option settings to improve performance, avoid numerical difficulties, control optimization run duration, or control output options.

Some problems solve faster with the primal simplex algorithm rather than the default dual simplex algorithm. Very few problems exhibit poor numerical performance in both the primal and the dual. Therefore, consider trying primal simplex if numerical problems occur while using dual simplex.

The barrier algorithm is an alternative to the simplex method for solving linear programs. It employs a primal-dual logarithmic barrier algorithm which generates a sequence of strictly positive primal and dual solutions. Specifying the barrier algorithm may be advantageous for large, sparse problems.

Cplex provides a sifting algorithm which can be effective on problems with many more variables than equations. Sifting solves a sequence of LP subproblems where the results from one subproblem are used to select columns from the original model for inclusion in the next subproblem.

GAMS/Cplex also provides access to the Cplex Conflict Refiner previously known as IIS (Irreducibly Inconsistent Set). The conflict refinder takes an infeasible program and produces an set of conflicting constraints. Such a set consists of constraints and variable bounds which is infeasible but becomes feasible if any one member of the set is dropped. GAMS/Cplex reports the conflict in terms of GAMS equation and variable names and includes the conflict report as part of the normal solution listing.

QP models are a special case that can be reformulated to have a quadratic objective function and only linear constraints. Those are automatically reformulated from GAMS QCP models. When such problems are convex, Cplex normally solves them efficiently in polynomial time. Nonconvex QPs, however, are known to be quite hard. Cplex applies various approaches to those problems, such approaches as barrier algorithms or branch and bound algorithms. Notably, in the branch and bound approach, there is no theoretical guarantee about the complexity of such a problem. Consequently, solution of such a problem (that is, a nonconvex QP) can take many orders of magnitude longer than the solution of a convex QP of comparable dimensions. Therefore, the default is to reject such model. The parameter OptimalityTarget allows to change this behavior.

The methods used to solve pure integer and mixed integer programming problems require dramatically more mathematical computation than those for similarly sized pure linear programs. Many relatively small integer programming models take enormous amounts of time to solve.

For problems with integer variables, Cplex uses a branch and cut algorithm which solves a series of LP, subproblems. Because a single mixed integer problem generates many subproblems,even small mixed integer problems can be very compute intensive and require significant amounts of physical memory.

Cplex can also solve problems of GAMS model type MIQCP. As in the continuous case, if the base model is a QP the Simplex methods can be used and duals will be available at the solution. If the base model is a QCP, only the Barrier method can be used for the nodes and only primal values will be available at the solution.

The Conflict Refiner identifies the causes of infeasibility by means of inconsistent set of constraints. However, you may want to go beyond diagnosis to perform automatic correction of your model and then proceed with delivering a solution. One approach for doing so is to build your model with explicit slack variables and other modeling constructs, so that an infeasible outcome is never a possibility. An automated approach offered in GAMS/Cplex is known as FeasOpt (for Feasible Optimization) and turned on by parameter FeasOpt in a CPLEX option file. More details can be found in the section entitled Using the Feasibility Relaxation.

This section introduces the solution pool for storing multiple solutions to a mixed integer programming problem (MIP and MIQCP). The chapter also explains techniques for generating and managing those solutions.

The solution pool stores multiple solutions to a mixed integer programming (MIP and MIQCP) model. With this feature, you can direct the algorithm to generate multiple solutions in addition to the optimal solution. For example, some constraints may be difficult to formulate efficiently as linear expressions, or the objective may be difficult to quantify exactly. In such cases, obtaining multiple solutions will help you choose one which best fits all your criteria,including the criteria that could not be expressed easily in a conventional MIP or MIQCP model. For example,

There are two ways to fill the solution pool associated with a model: You can accumulate successive incumbents or generate alternative solutions by populating the solution pool. The method is selected with the parameter SolnPoolPop:

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