Transforming Optimization Code from Problem-Based to Solver-Based Approach in MATLAB
4 views (last 30 days)
Show older comments
Hello,
I have successfully implemented an ILP problem using MATLAB's problem-based optimization approach. However, I am now looking to switch to the solver-based approach to take advantage of its flexibility and efficiency.
In the problem-based approach, I defined binary decision variables, constraints, and an objective function using optimvar, optimconstr, and optimexpr. The code works well, but I need guidance on how to transform this code into a solver-based format using intlinprog.
Here’s a summary of what I have:
Decision Variables:
A 3D binary matrix A(N, numNodes, num_vehicles) for task assignment.
A binary vector chi(num_vehicles) for satisfaction.
Other binary variables like z, t_wait_aux, etc.
Constraints:
Assignment constraints, dependency constraints, and time-based constraints.
I linearized expressions using auxiliary variables and max constraints.
Objective Function:
The objective is to maximize the satisfaction rate, expressed as a linear function of chi.
Could you provide guidance or directions how to systematically convert my existing problem-based variables and constraints into the matrix form required by intlinprog
Here is the code
the problem is attached.
Thank you for your continued help!
run('MyProblem.m')
6 Comments
Answers (1)
Matt J
on 16 Aug 2024
See prob2struct
3 Comments
Matt J
on 16 Aug 2024
Edited: Matt J
on 16 Aug 2024
@Maria That's not what @Torsten said. He said that he doesn't recommend moving to solver-based form at all because it is hard to make changes once the problem is in that form.
If solve() works on the problem-based formulation then prob2struct has to work. All that solve() really does internally is convert to solver-based form for you (probably using prob2struct) and then run a traditional solver.
See Also
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!