Read the portion of the research paper below, then summarize and analyze which algorithm design does it discusses, which one is better in general and under which conditions

An optimisation problem can be solved by dynamic programming if an optimal solution is composed of optimal solutions to subproblems. This property, which is known as the principle of optimality, can be formalised as a monotonicity condition. If the principle of optimality is satisfied, one can compute a solution by decomposing the input in all possible ways, recursively solving the subproblems, and then combining optimal solutions to subproblems into an optimal solution for the whole problem. By contrast, a greedy algorithm considers only one decomposition of the argument. This decomposition is usually unbalanced, and greedy in the sense that at each step the algorithm reduces the input as much as possible. If the decomposition has a more balanced character, the algorithm is commonly classified as an instance of the divide$-$and$-$conquer paradigm.

Certain greedy algorithms can be scen as refinements of dynamic programming. If the principle of optimality is satisfied, dynamic programming is applicable, and if an additional monotonicity condition is satisfied, then we can narrow the choice of decompositions to a single candidate, thus obtaining a greedy algorithm. This idea was formalised in $[3]$ for a restricted class of optimisation problems, namely those involving list partitions. Although it was suggested that the technique might be more widely applicable, it proved difficult to formulate the general theorem in the framework of that paper. The conclusion