Sentence examples for bound for the objective from inspiring English sources

We address nonconvex bilinear problems where the main objective is the computation of a tight lower bound for the objective function to be minimized.

Success in finding the global optimal solution is highly dependent on the quality of the relaxation that is used to compute the lower bound for the objective of minimizing the total flowrate going through the treatment units.

To measure the quality of the solutions given by the combination of a constructive heuristic followed by a local search, we define a lower bound for the objective function that counts the number of bad rows in a solution, i.e. a lower bound on the minimal number of rows to remove from the matrix so that, for the remaining rows, there is an order of the columns with no bad row.

An auxiliary function has been designed, that incorporates some desired characteristics in order to provide an upper bound for the objective function, which summarizes the multiclass classification task.

Step 3: In step2, we compute an upper bound for the objective function value.

Step 10: Modify the coefficient of compensation, relative importance (( theta_{k} )), or the membership functions by considering only the following variations: a) an increase in the lower bound for the maximization objective and b) a decrease in the upper bound for the minimization objective; then, go to Step 7. Otherwise, go to the next step.

We formulate this problem as an integer-programming problem and present a Lagrangian relaxation method which, for each choice of Lagrangian multipliers, provides a lower bound for the optimal objective function value.

Then, the relative gap for this solution measures the distance between z B and the available best bound for the optimal objective function value (z L ) using the ratio | z B − z L | z L. LP-based branch-and-bound algorithms are used for solving MIPs in GAMS [31]; thus, z L is the LP relaxation solution of the MIP problem under consideration.

It means that for higher values of γ, the solution method results bigger lower bound for the satisfaction degrees of objectives (λ0) for a given sample example.

To increase the computation speed, a lower bound of the objective value for each route design solution is derived and used in the fitness evaluation of the proposed algorithm.

The LR can be seen as a broad range of techniques which supplies a lower bound of the objective function and good starting points for heuristic searches to obtain feasible primal solutions.