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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, ...


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 ...


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 $\...


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 ...


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; }


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 ...


with cplexAPI you may use addQConstrCPLEX to add quadratic constraints


This document available on link https://cran.r-project.org/web/packages/Rcplex/Rcplex.pdf for RCplex might be helpful for you. Page 7 & 8 has an example of QCP.

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