A set of values of decision variables which satisfies the linear constraints and nn-negativity conditions of a L.P.P. is called its

The correct option is Feasible solution.

In a Linear Programming Problem (L.P.P.), we deal with optimizing (maximizing or minimizing) a linear objective function subject to a set of linear constraints and non-negativity restrictions on the decision variables.

Let us understand the terminology associated with the solutions of an L.P.P. to see why "Feasible solution" is the correct term:
1. Decision Variables: These are the variables (usually denoted by _x1, _x2, ..., _xn) whose values we need to determine.
2. Constraints: These are the linear inequalities or equations that limit or restrict the values of the decision variables.
3. Non-negativity Restrictions: These are conditions requiring all decision variables to be non-negative, i.e., $$x_i\ge 0$$ for all _i.
4. Feasible Solution: Any set of values of the decision variables that simultaneously satisfies all the given constraints and the non-negativity conditions is called a feasible solution. The region containing all such feasible points is called the feasible region.
5. Optimal (or Optimum) Solution: A feasible solution that also optimizes (maximizes or minimizes) the objective function is called an optimal solution.
6. Unbounded Solution: If the value of the objective function can be increased or decreased indefinitely without violating any constraints, the L.P.P. is said to have an unbounded solution.

Since the question asks for the name given to a set of values of decision variables that satisfies the linear constraints and non-negativity conditions, it fits the definition of a feasible solution.