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TEST BORRADO, QUIZÁS LE INTERESETest1 Heuristics

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Título del test:
Test1 Heuristics

Descripción:
Heuristica y Optimizacion

Autor:
AVATAR

Fecha de Creación:
05/11/2020

Categoría:
Universidad

Número preguntas: 20
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Temario:
Slack variables are used to: Force conversion to constraints of type = when they are of types <= or> = Force the appearance of the identity matrix I to start the Simplex application Force the base to have the proper dimensions to start the Simplex application.
In linear programming, the transformation of an equality: It gives rise to a single restriction: either of type <= or of type> = It gives rise to three restrictions: one of type <=, another of type> = and another of type = It gives rise to two restrictions: one of type <=, another of type> =.
A transportation problem is characterized by: It consists of a Linear Programming task that will surely have decision variables thatthey can take any value, but necessarily integer It consists of a bipartite graph of origins and destinations in which it is necessary to decide if objects of aplace to another It consists of a bipartite graph of origins and destinations in which the number of objects must be decidedthat move from one place to another.
A linear programming problem can be described in several ways: Singular, standard and symmetric Canonical, standard and symmetric Canonical, multi-valued and standard.
If a linear programming problem has more constraints than decision variables: That is not possible and the modeling is wrong It is normal, and the Simplex can proceed normally to its resolution. It's infeasible.
The model of graphical resolution of linear programming problems: Only good for solving problems with few restrictions Only valid for solving problems with few variables It is a convenient method to solve problems of any size.
Consider solving a Linear Programming task with the \ textsc {Simplex} algorithm that contains theconstraint X1 + 6x2 <= 4. If in any iteration the point (2,1) has been obtained: Then we know that the point (2,1) is an extreme point of the feasible region Sure it is possible and in fact it means the problem is infeasible That is necessarily impossible.
Which of the following constraints is linear? 2x1 + x2 + 1 <> 3 (with <> meaning "different") x1 ^ 2 + 2x2 + 1 = 3 2x1 + x2 + 1 = 3.
Artificial variables are used to: Force conversion to constraints of type = when they are of types <= Ó> = Force the base to have the proper dimensions to start the Simplex application Force the appearance of the identity matrix I to start the Simplex application.
Mark the correct phrase: The primal problem of the dual problem is the primal problem The dual problem of the primal problem is the primal problem The dual problem of the dual problem is the primal problem.
In a linear programming problem: Constraints must be modeled as <=,> = Ó = Constraints can be freely modeled in any way Constraints can be modeled as <, <=,>,> = Ó =.
The maximum value \ of the objective function in a feasible region F, zF is, relative to the maximum value of the functionobjective in a subregion F1, zF1 Necessarily less than or equal, zF <= zF1 Equal, lower or higher Necessarily greater than or equal, zF> = zF1.
The decision variables of a linear programming problem: They must all be non-negative They must all be non-positive They can be both positive and negative.
Applying the branching and dimensioning model in depth to solve linear programming problems whole: The feasible region is divided into others, eliminating restrictions from the original problem The feasible region is divided into others by modifying the objective function The feasible region is divided into others by adding restrictions to the original problem.
If it was not necessary to add artificial variables to a Linear Programming task to start the application of theSimplex with a base equal to the identity matrix: The problem can be infeasible The problem necessarily has an infinite number of solutions The problem is necessarily doable (factible).
When solving a linear programming problem with the use of the Simplex with n variables and m constraints,with m <= n It is mandatory to find an initial basis of dimension B mxm that provides some solution to the problem of linear programming Bx = b that, in addition, only gives positive or null solutions to the decision variables x It is mandatory to find an initial basis of dimension B mxm that provides some solution to the problem oflinear programming Bx = b It is mandatory to find an initial basis of dimension B nxn that gives some solution to the problem of linear programming Bx = b that, in addition, only gives positive or null solutions to the decision variables x.
If a Linear Programming problem consists only of constraints of the form <= o = then: The problem is certainly feasible The problem may or may not be feasible The problem is certainly not bounded.
A linear programming problem is unbounded if: If at the end of the Simplex some artificial variable has a positive value If when calculating the leavingrule all the values ​​of the vector yj are negative or null In a Simplex step, some decision variable takes a negative value.
Once a LPT has been solved, the slack variables must be interpreted as they have a specific meaning: If a slack variable is added with a positive value, this value represents the increment of the objective function by unit of resource If a slack variable is added with a positive value, this value represents the quantity of resources that are missing (faltan) If a slack variable is added with a positive value,this value represents the quantity of resources that left over (sobran).
Each time an artificial variable and a slack variable are added to a constraint of a maximization problem, you must: Add the artificial variable to the objective function with minus infinite cost and the slack variable of zero cost Doing nothing Add the slack variable to the objective function with minus infinite cost and the artificial variable of zero cost .
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