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Our contribution in this paper is two folded. We consider first the case of linear programming with real coefficients and give a method which allows the computation of a new upper bound on the distance from the origin to a feasible point. Next we present an application of the ellipsoid method to form a novel algorithm which allows one to search for the continuous convex functions minimum without the prior knowledge on the search radius. If stopped early, the proposed algorithm can give proofs that the optimal value has not been reached yet, hence the user can opt for more iterations. However, if prior guarantees exist on the existence of an optimum point in a given ball, the algorithm is guaranteed to find it. For such a case we prove a polynomial upper bound on the number of flops required to obtain the solution. The presented algorithm is then applied to linear programming. We further develop our algorithm and provide a method to answer the feasibility question of linear programs with real coefficients in a number of flops bounded above by a polynomial in the number of variables and the number of constraints. However, in case of feasibility, our method does not generate an actual feasible point. For obtaining such a point, we have to make some assumptions on the subgradients of a certain function.