# Tag Info

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Has anyone tested this approach on a real world business problem? If the question is, more generally, "for a practical optimization problem, can ML somehow accelerate the performance of a state-of-the-art MIP solver, given that we have already solved a large number of similar instances in the past?", then the answer is yes. In the reference below, ...

6

Integer programming models solved by CPLEX (or most other solvers) require linear or, in certain very limited cases, quadratic constraints. Your constraint 9 involves dividing a parameter (h) by an integer variable (man), which results in a nonlinear expression. Multiplying both sides by man will not help, since that produces an equation with products of ...

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Consider maximizing $\sum_{i=1}^n x_i$ subject to $x$ binary and $\sum_{i=1}^n i x_i \leq 2$ Solve relaxation and it gives $x_1 = 1, x_2 = 1/2$ with remaining variables 0. Branch on $x_2 = 0$ and the new solution is $x_1 = 1, x_3 = 1/3$ with the rest 0. Continue in the same fashion and you will have to go through fixing all variables. Had you minimized \$\...

4

When you turn CPLEX loose on a model, it runs a presolver that does assorted magic tricks that end up with a modified model. It then solves the modified model and, assuming it finds a solution to the modified model, transforms that solution back to the original model. I believe that the "unscaled infeasibilities" message means that CPLEX found what ...

3

In OPL CPLEX you could start with int g=10; range G=1..g; range I=1..5; {int} Ng[G]=[{1,2},{3,4},{5},{},{}, {},{},{},{},{}]; dvar boolean u[G]; subject to { forall(i in I) sum(g in G:i in Ng[g]) u[g]==1; }

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I am fairly certain that you cannot resume solution from the previous final state after altering the model. This is a known fact with CPLEX, presumably the same with CPOptimizer for essentially the same reason: the final state of the previous solve may not be valid for the modified problem. For instance, if you were to drop constraints and resume, the true ...

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