A convex cone is a subset of a vector space that is closed under linear combinations with non-negative scalars, meaning if two vectors are in the set, any non-negative linear combination of them is also in the set. This property makes convex cones fundamental in optimization, particularly in linear programming and conic optimization, where they help define feasible regions and constraints.