Mosek solver for linear, mixed integer programming and non linear convex optimization |
About MosekThe MOSEK Optimization Software is designed to solve large-scale mathematical optimization problems. MOSEK provides specialized solvers for linear programming, mixed integer programming and many types of nonlinear convex optimization problems. The state-of-art conic quadratic (aka. SOCP) optimizer in MOSEK makes it ideal for financial applications such as portfolio optimization.
MOSEK can solve:
Learn more about Mosek optimization software
About OptimJ solver link for Mosek
OptimJ™ for Mosek lets you develop, debug and tune models in Java™ using state-of-the-art tools and techniques. It provides a clear and concise algebraic notation for optimization modeling, object-oriented programming for data modeling, and powerful bulk data manipulation primitives for pre- and post-processing.
OptimJ™ models are directly compatible with Java™ source code, existing Java libraires such as database access, Excel connection or graphical interfaces, leveraging existing code bases and training, and facilitating communication between optimization experts and IT teams.
OptimJ™ brings modern development tools such as Eclipse, CVS, JUnit or JavaDoc to optimization experts, improving productivity and quality.
You can try OptimJ™ for Mosek with a free 30-days evaluation licence including examples of OptimJ models for the Mosek solver.
Ateji and Mosek offer an attractive bundle price of OptimJ™ and Mosek Optimization Software. Please contact us for the details.
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Customer Quotes
We're going to deploy OptimJ capabilities for our ongoing Java-based projects to close a gap between optimization engines and Java applications.
Andrey Torzhkov,
Research Scientist,
Siemens Corporate Research.
With OptimJ you get the expressiveness of OPL™ with the integrability and flexibility of Ilog Concert™ -- the best of both worlds.
Luc Mercier,
Phd student,
Brown University.
Integrating optimization projects in a Java environment becomes a breeze using the Eclipse IDE, shortening project development times up to 50%.
OptimJ made it easy to use results from different solvers and combine exact methods with metaheuristics coded in Java, for solving complex industrial problems.
Médéric Suon,
Industrial Engineer,
I used OptimJ to implement a model for production planning in a polystyrene factory.
Luc Mercier,
Phd student,
Brown University.
We've succesfully applied OptimJ to improve an existing software application developed in one of our past numerical optimization projects.
Andrey Torzhkov,
Research Scientist,
Siemens Corporate Research.
Using OptimJ enabled a rapid development and integration of optimization models in Java-based applications.
Médéric Suon,
Industrial Engineer,
