Powerful Optimization for Complex Problems
Gurobi’s outstanding performance has been demonstrated through leadership in public benchmark tests and dramatic improvement in solve times year after year. Built using the latest algorithmic developments and large-scale computing techniques, Gurobi’s extremely robust code ensures correctness and scalability of results.
AMPL Brings You the Power of Gurobi
Gurobi Optimizer, integrated with AMPL, provides a powerful solution for solving complex optimization problems with unparalleled speed and reliability. Leverage the advanced capabilities of Gurobi through the intuitive and flexible AMPL modeling language.
AMPL Brings You the Power of Gurobi
Gurobi Optimizer is a state-of-the-art mathematical optimization solver renowned for its speed, reliability, and flexibility. It excels in solving a wide range of optimization problems, including linear programming (LP), mixed-integer programming (MIP), quadratic programming (QP), and more. Gurobi’s robust algorithms and advanced features make it a top choice for industries needing optimal solutions for complex problems.
Linear and quadratic optimization in continuous and integer variables, for both convex and nonconvex cases, with extensions to widely used nonlinear and logical expressions.
Convex and nonconvex quadratic expressions in objectives and constraints; conic quadratic constraints; convenient nonlinear and logical operators.
For continuous linear and convex quadratic problems, primal and dual simplex methods and interior-point (barrier) methods. For integer problems, advanced branch-and-cut with presolve, feasibility heuristics and cut generators. For general nonlinear problems, spatial branch-and-bound and outer approximation.
Global optimization of nonconvex quadratic and select nonlinear problems. Shared-memory processing for barrier and for branch-and-cut. Distributed concurrent optimization and tuning to leverage multiple machines. Streamlined access to cloud services. Special facilities for multi-objective optimization and infeasibility diagnosis.
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Gurobi Optimization, founded in 2008, is a renowned developer of advanced software for linear, mixed-integer, quadratic, and more general nonlinear optimization. Its flagship product, the Gurobi Optimizer, is highly regarded for its exceptional combination of speed and robustness in determining optimal solutions for problems in thousands to millions of decision variables.
Gurobi was created by three prominent experts in the optimization field — Dr. Zonghao Gu, Dr. Edward Rothberg, and Prof. Robert Bixby — who sought to create a high-performance solver that could address the growing demands of complex data-driven decision-making, while integrating readily with popular programming languages and platforms. This adaptability has made Gurobi a preferred choice in numerous industries, including logistics, finance, energy, telecommunications, and manufacturing.
The Gurobi Optimizer has continuously evolved, integrating cutting-edge research and algorithms to maintain its position at the forefront of optimization technology, expanding the variety of problem types addressed, and broadening the range of on-premise and cloud-based solutions available. Gurobi’s commitment to innovation, coupled with strong customer support and community engagement, underscores its role as a key player in advancing the practice of mathematical optimization.
Linear and quadratic optimization in continuous and integer variables, for both convex and nonconvex cases, with extensions to widely used nonlinear and logical expressions.
Convex and nonconvex quadratic expressions in objectives and constraints; conic quadratic constraints; convenient nonlinear and logical operators.
For continuous linear and convex quadratic problems, primal and dual simplex methods and interior-point (barrier) methods. For integer problems, advanced branch-and-cut with presolve, feasibility heuristics and cut generators. For general nonlinear problems, spatial branch-and-bound and outer approximation.
Global optimization of nonconvex quadratic and select nonlinear problems. Shared-memory processing for barrier and for branch-and-cut. Distributed concurrent optimization and tuning to leverage multiple machines. Streamlined access to cloud services. Special facilities for multi-objective optimization and infeasibility diagnosis.
