Leverage CONOPT’s Strengths with AMPL
A proven choice for highly nonlinear problems, CONOPT’s efficient and reliable multi-method architecture handles a broad range of models. Specialized techniques achieve feasibility quickly, while powerful preprocessing tools reduce problem size and suggest formulation improvements.
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Current Developer: GAMS
Original Developer: ARKI Consulting & Development A/S
Current version: 3.17A
CONOPT is a robust and widely used solver specifically designed for tackling large-scale nonlinear optimization problems. Its strength lies in its ability to handle various types of nonlinearities, both in the objective function and constraints. This makes it particularly well-suited for complex optimization problems in diverse fields like engineering, economics, and finance.
CONOPT employs a combination of powerful algorithms, including the feasible-path generalized reduced gradient (GRG) method and sequential quadratic programming (SQP). This blend enables it to efficiently navigate complex problem landscapes, often finding optimal solutions or identifying infeasibility when constraints cannot be satisfied.
Furthermore, CONOPT boasts several features that enhance its problem-solving capabilities. It pre-processes models to identify and eliminate redundant elements, improving computational efficiency. Additionally, it can leverage second-order derivatives, leading to faster convergence and more accurate solutions in certain cases. While CONOPT might not be the optimal choice for every problem, its versatility, efficiency, and ability to handle challenging nonlinearities make it a valuable tool in the optimization toolbox.
Linear, quadratic, and general smooth nonlinear objectives and constraints in continuous variables.
Dynamic selection among feasible-path generalized reduced gradient, sequential quadratic programming, and sequential linear programming.
Extensions to the basic algorithms take advantage of second derivatives and identify feasible solutions more reliably.