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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Developed by ARKI Consulting & Development, 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.
For Python enthusiasts, CPLEX is also accessible as a module with amplpy, blending seamlessly with your Python projects. Explore more about this integration at AMPL Python Integration.# Install Python API for AMPL
$ python -m pip install amplpy
# Install CONOPT
$ python -m amplpy.modules install conopt
Best for individuals running on one machine
$3,000 /year
$2,000 /year
Best for small applications running one process at a time
$4,500 /year
$3,000 /year
Best for large teams or applications to run multiple processes simultaneously
$7,000 /year
+$700 /additional CPU
$4,000 /year
+$400 /additional CPU
Best for individuals running on one machine
$6,000 /purchase
+ $1,200 maintenance annually
$4,000 /purchase
+ $800 maintenance annually
Best for small applications running one process at a time
$9,000 /purchase
+ $1,800 maintenance annually
$6,000 /purchase
+ $1,200 maintenance annually
Best for large teams or applications to run multiple processes simultaneously
$14,000 /purchase
+ $2,400 maintenance annually
+1,400 /additional CPU
$8,000 /purchase
+ $1,600 maintenance annually
+800 /additional CPU
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Best for individuals running on one machine
$3,000 /yearly subscription
$6,000 /purchase
+ $1,200 maintenance annually
$2,000 /yearly subscription
$4,000 /purchase
+ $800 maintenance annually
Best for small applications running one process at a time
$4,500 /yearly subscription
$9,000 /purchase
+ $1,800 maintenance annually
$3,000 /yearly subscription
$6,000 /purchase
+ $1,200 maintenance annually
Best for large teams or applications to run multiple processes simultaneously
$7,000 /yearly subscription
+ $700 additional CPU
$14,000 /purchase
+ $2,800 maintenance annually + $1,400 additional CPU
$4,000 /yearly subscription
+ $400 additional CPU
$8,000 /purchase
+ $1,600 maintenance annually + $800 additional CPU
CONOPT downloads are available from the My Downloads page of your account at the AMPL Portal, and are included in the bundles that are used for free trials.
CONOPT shines as a powerful optimization solver for complex, nonlinear problems involving continuous variables. Its versatility allows it to handle various nonlinearities in both the objective function and constraints, making it suitable for diverse fields like engineering, economics, and finance. CONOPT’s strengths lie in its efficient algorithms, combining techniques like feasible-path generalized reduced gradient and sequential quadratic programming to navigate intricate problem landscapes. Additionally, it boasts features like model pre-processing and the ability to utilize second-order derivatives, leading to faster computation and more accurate solutions. However, CONOPT is not a one-size-fits-all solution.
Here’s where AMPL comes in. AMPL, an algebraic modeling language, acts as a bridge between users and powerful solvers like CONOPT. It allows users to define their optimization problems in a clear, concise, and human-readable format, independent of the specific solver used. This significantly simplifies the modeling process and streamlines the workflow. Furthermore, AMPL integrates seamlessly with CONOPT, enabling users to leverage CONOPT’s capabilities without delving into its intricate details. This powerful combination empowers users to tackle complex optimization problems efficiently and effectively.
CONOPT is a powerful solver specifically designed for tackling large-scale nonlinear optimization problems. This includes problems with continuous variables where the objective function and/or constraints involve non-linear relationships.
The combination of CONOPT and AMPL offers several advantages:
No. AMPL, as an algebraic modeling language, allows you to define your optimization problem in a clear, human-readable format, eliminating the need for complex coding.
CONOPT offers various features to enhance problem-solving, including:
AMPL provides extensive documentation and tutorials specifically dedicated to using CONOPT. You can find resources on the AMPL website, including examples, user guides, and FAQs.
AMPL provides extensive documentation and tutorials specifically dedicated to using CONOPT. You can find resources on the AMPL website, including examples, user guides, and FAQs.
ARKI Consulting & Development website
Using-CONOPT-with-AMPL: Guide and option listing
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.
CONOPT downloads are available from the My Downloads page of your account at the AMPL Portal, and are included in the bundles that are used for free trials.
Developer: ARKI Consulting & Development A/S
Current version: 3.17A
Problem types supported: Linear, quadratic, and general smooth nonlinear objectives and constraints in continuous variables.
Algorithms available: Dynamic selection among feasible-path generalized reduced gradient, sequential quadratic programming, and sequential linear programming.
Special features: Extensions to the basic algorithms take advantage of second derivatives and identify feasible solutions more reliably.
ARKI Consulting & Development website
Using-CONOPT-with-AMPL: Guide and option listing