In general, subset sum problem is strongly believed to be computationally difficult to solve. But in 1983, Lagarias and Odlyzko proposed a probabilistic algorithm for solving subset sum problems of sufficiently low density in polynomial time. In 1991, Coster et. al. improved the Lagarias-Odlyzko algorithm and solved subset sum problems with higher density. Both algorithms reduce subset sum problem to finding shortest non-zero vectors in special lattices. In this thesis, we first proposed a new viewpoint to define the problems which can be solved by this two algorithms and shows the improved algorithm isn't always better than the Lagarias-Odlyzko algorithm. Then we verify this notion by experimentation. Finally, we find that the Lagrias-Odlyzko algorithm can solve the high-density subset sum problems if the weight of solution is higher than 0.7733n or lower than 0.2267n, even the density is close to 1.

A well known birthday paradox is one the most important problems in cryptographic applications. Incremental hash functions or digital signatures in public key cryptography and low-weight parity check equations of LFSRs in stream ciphers are examples of such applications which benet from birthday problem theories to run their attacks. Wagner introduced and formulated the k-dimensional birthday problem and proposed an algorithm to solve the problem in O(k.m^ 1/log k ). The generalized birthday solutions used in some applications to break Knapsack based systems or collision nding in hash functions. The optimized birthday algorithms can solve Knapsack problems of dimension n which is believed to be NP-hard. Its equivalent problem is Subset Sum Problem nds the solution over Z/mZ. The main property for the classication of the problem is density. When density is small enough the problem reduces to shortest lattice vector problem and has a solution in polynomial time. Assigning a variable to each element of the lists, decoding them into a matrix and considering each row of the matrix as an equation lead us to have a multivariate polynomial system of equations and all solution of this type can be a solution for the k- list problem such as F4, F5, another strategy called eXtended Linearization (XL) and sl. We discuss the new approaches and methods proposed to reduce the complexity of the algorithms. For particular cases in over-determined systems, more equations than variables, regarding to have a single solutions Wolf and Thomea work to make a gradual decrease in the complexity of F5. Moreover, his group try to solve the problem by monomials of special degrees and linear equations for small lists. We observe and compare all suggested methods in this

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