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What are decision variables in LP?

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Decision variables represent the quantities that you want to determine in an LP problem. They are the unknowns you need to solve for in order to optimize the objective function while satisfying all constraints.

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In the context of linear programming (LP), decision variables are the unknown quantities that the model aims to find in order to maximize the objective function-based outcome. These variables, which include how much of a product to produce, how many resources to allot, and how to schedule tasks, indicate the options available to the decision-maker. Every decision variable has a numerical value that influences the overall goal, which could be cost minimization or profit maximization. The feasible zone where the best solution can be discovered is defined by the decision variables' values meeting the problem's constraints.

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Decision variables in linear programming are the unknowns that decision-makers seek to determine in order to optimize the objective function, representing the quantities to be decided upon within the constraints

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the unknown quantities that are expected to be estimated as an output of the LPP solution.

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