Proving feasibility of convex linear combination in LP problem

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In the discussion on proving the feasibility of a convex linear combination in a linear programming (LP) problem, it is established that if x1, x2, ..., xk are feasible solutions, then any convex combination v of these solutions is also feasible. The feasibility is demonstrated by showing that v, defined as v = α1x1 + α2x2 + ... with αi ≥ 0 and Σαi = 1, satisfies the constraints of the LP problem. Specifically, it is shown that Av ≤ b holds true, confirming that v remains within the feasible region. Additionally, it is noted that v maintains non-negativity, fulfilling all necessary conditions for feasibility. This conclusion reinforces the properties of convex combinations in linear programming contexts.
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Let x1, . . . , xk be feasible solutions of the linear programming problem:

Maximize z = (c^t)*x subject to Ax < = b and x >= 0,
so for i= 1, . . . , k, Axi <=b and xi >=0,

Let v be any convex linear combination of x1, . . . , xk.

i want to show that v is also a feasible solution of the problem..does anyone know how to show this
 
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Think about the definition of "feasible solution" and show that any convex linear combination satisfies it.
 
Let v = \alpha_1 x_1 + \alpha_2 x_2 + \cdots, where \alpha_i \geq 0, \forall i are real numbers in which \alpha_1 + \alpha_2 + \cdots = 1.

Then
\alpha_1 A x_1 + \alpha_2 A x_2 + \cdots \leq \alpha_1 b + \alpha_2 b + \cdots.

Hence
Av \leq b.

Note also that v \geq 0.
 
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