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Abstract

A fundamental theorem of linear programming states that a feasible linear program is solvable if and only if its objective function is copositive with respect to the recession cone of its feasible set. This paper demonstrates that this crucial guarantee does not extend to Second-Order Cone Programs (SOCPs), a workhorse model in robust and convex optimization. We construct and analyze a rigorous counterexample derived from a robust linear optimization problem with ellipsoidal uncertainty. The resulting SOCP possesses a non-empty feasible set, a bounded objective, and an objective function that is copositive on its recession cone. Despite satisfying these classical conditions for solvability, the problem admits no optimal solution; its infimum is finite but unattainable. We trace this pathology directly to the non-polyhedral geometry of the second-order cone, which causes the image of the feasible set under the linear objective to be non-closed. We interpret the example explicitly within the context of robust optimization, discuss its significant practical implications for modeling and computation, and propose effective mitigation strategies via polyhedral approximation or regularization.