Linear Complementarity Problem

Linear Complementarity Problem (LCP) is a generalization of Linear Programming and a special case of quadratic programming. I stumbled upon LCP theory due to my interest in complexity problems in game theory and PPAD-completeness. As we will see these concepts are very closely related.

Let M be a n \times n square matrix and q an n dimensional vector. Let LCP(q,M) be the following problem : Find two vectors w and z satisfying


LCP(q,M) consists of linear constraints and complementary conditions. Since w{\geq}0, z{\geq}0 the complementary conditions {w_i}{z_i}=0 is equivalent to {w^T}{z}=0. There is an obvious exponential time algorithm to solve LCP. For every i, set either w_i=0 or z_i=0 and solve the resulting system of linear equations. If one of these linear systems has a solution then the corresponding LCP is solvable. Deciding if a given LCP has a solution is NP-complete. The following exercise shows that LCP is a generalization of LP.

Exercise : Every LP can solved by solving a corresponding LCP, representing the complementary slackness of the LP.

LCP can also be expressed in the following equivalent form :


Lemke’s algorithm is a “path-following” algorithm (similar to simplex algorithm) to solve LCP. Unfortunately, Lemke’s algorithm can sometimes fail to produce a solution even if one exists !! There are many special instances of LCP on which Lemke’s algorithm always produces a solution or a certificate that no solution exists.

As mentioned earlier, solving an LCP is NP-complete. What about special cases ? i.e., when the input matrix M is special.

  • If M is a Positive Semi-Definite matrix, then LCP(q,M) can be solved in polynomial time. In fact, every LCP with a PSD matrix is a convex quadratic program and every convex quadratic program can be expressed as an LCP with a PSD matrix.
  • If M is a Z-matrix, Chandrasekaran’s algorithm solves LCP(q,M) in polynomial time [Chandrasekaran’70].
  • If M is a triangular P-matrix, LCP(q,M) can be solved in polynomial time by using a back substitution method.
  • If M is a P-matrix, LCP(q,M) has a unique solution for every q.

Following is one of the coolest applications of LCP.

Exercise : Finding a Nash Equilibrium in a bimatrix game can be expressed as an LCP.

Lemke-Howson’s algorithm [Lemke,Howson’64] to solve a bimatrix game is known to take exponential number of steps in the worst case [Savani, vonStengel’04]. It is also known that finding Nash equilibrium in a bimatrix game is PPAD-complete [Chen,Deng’09].

Open Problems :

  • The complexity of solving LCP with a P-matrix (P-LCP) is open for more than two decades !! P-LCP is known to be in PPAD [Papadimitriou’94]. Note that recognizing Z-matrices and PSD-matrices can be done in polynomial-time but recognizing P-matrices is coNP-complete [Coxson’94].
  • Are there other interesting classes of matrices M for which LCP(q,M) is solvable in polynomial time ?
  • Savani and von Stengel’s instance of bimatrix game has “full support mixed equilibrium”, which can easily solved using linear programming techniques. It is an open problem to construct an instance of a bimatrix game that does not have full-support mixed equilibrium and the Lemke-Howson algorithm takes exponential number of steps on this instance.
    Savani and von Stengel’s instance of bimatrix game has full support.
    It is open problem to construct an instance of bimatrix game that
    does not have full-support mixed equilibrium and the Lemke-Howson algorithm
    takes exponential number of steps.

References :

  • [Chandrasekaran’70] R. Chandrasekaran. “A Special Case of the Complementary Pivot Problem“, Opsearch, 7(1970) 263 268.
  • [Coxson’94] G. E. Coxson. The P-matrix problem is co-NP-complete. Math. Programming, 64(2):173–178, 1994.
  • [Chen,Deng’09] Xi Chen, Xiaotie Deng, Shang-Hua Teng: Settling the complexity of computing two-player Nash equilibria. J. ACM 56(3): (2009)
  • [Savani, vonStengel’04] Rahul Savani, Bernhard von Stengel: Exponentially Many Steps for Finding a Nash Equilibrium in a Bimatrix Game. FOCS 2004: 258-267
  • [Lemke,Howson’64] Lemke, C. E. and J. T. Howson, Jr. (1964), Equilibrium points of bimatrix games. Journal of the Society for Industrial and Applied Mathematics 12, 413–423.
  • [Papadimitriou’94] Christos H. Papadimitriou: On the Complexity of the Parity Argument and Other Inefficient Proofs of Existence. J. Comput. Syst. Sci. 48(3): 498-532 (1994)

Held Karp Relaxation

The Traveling Salesman Problem (TSP) is undoubtedly the most important and well-studied problem in Combinatorial Optimization. Today’s post is a quick overview of the Held-Karp Relaxation of TSP.

TSP : Given a complete undirected graph G(V,E) with non-negative costs c_e for each edge e \in E, find a hamiltonian cycle of G with minimum cost. It is well-known that this problem is NP-Complete.

Exercise : There is no \alpha-approximation algorithm for TSP (for any \alpha \geq 1) unless P=NP.

Metric TSP : In Metric-TSP, the edge costs satisfy triangle inequality i.e., for all u,v,w \in V, c(u,w) \leq c(u,v) + c(v,w). Metric-TSP is also NP-complete. Henceforth, we shall focus on metric TSP.

Symmetric TSP (STSP) : In STSP, the edge costs are symmetric i.e., c(u,v) = c(v,u). Approximation algorithms with factor 2 (find a minimum spanning tree (MST) of G and use shortcuts to obtain a tour) and factor 3/2 (find an MST, find a perfect matching on the odd degree nodes of  the MST to get a eulerian graph and obtain a tour) are well-known. The factor 3/2 algorithm, known as Christofides Algorithm [Christofides’76], is the best known approximation factor for STSP. No improvement in the last three decades !!

Following is the Held-Karp Relaxation for STSP with the cut constraints and the degree constraints. The variables are x_e, one for each edge e \in E. For a subset S \subset V, \delta(S) denotes the edges incident to S. Let x(\delta(S)) denote the sum of values of x_e of the edges with exactly one endpoint in S. For more details of Held-Karp relaxation see [HK’70, HK’71]


Exercise : In the following instance of STSP the cost between vertices u and v is the length of the shortest path between u and v. The three long paths are of length k. Prove that this instance achieves an integrality ratio arbitrarily close to 4/3 (as k is increased).


Asymmetric TSP (ATSP) : In ATSP, the edge costs are not necessarily symmetric i.e., the underlying graph is directed. The Held-Karp relaxation for ATSP is as follows :


Charikar, Goemans and Karloff [CGK’04] showed that the integrality of Held-Karp relaxation for ATSP is at least 2-\epsilon. Frieze, Galbiati and Maffioli [FGM’82] gave a simple O({\log}_2{n})-approximation algorithm for ATSP in 1982, where n is the number of vertices. In the last eight years, this was improved to a guarantee of 0.999 {\log}_2{n} by Blaser [Blaser’02], and to \frac{4}{3}{\log}_3{n} Kaplan et al [KLSS’03] and to \frac{2}{3}{\log}_2{n} by Feige and Singh [FS’07]. So we have an approximation factor better than {\ln}n !!

Open Problems :

  • The long-standing open problem is to determine the exact integrality gap of Held-Karp relaxation. Many researchers conjecture that the integrality gap of Held-Karp relaxation for STSP is 4/3 and for ATSP it is bounded by a constant. The best known upper bounds are 3/2 and O(logn) respectively.
  • The size of the integrality gap instance of ATSP (constructed by [CGK’04]) is exponential in 1/\epsilon to achieve an integrality gap of 2-\epsilon. Is there a polynomial-sized (in 1/\epsilon) instance achieving an integrality gap of 2-\epsilon ?

References :

Nicos Christofides, Worst-case analysis of a new heuristic for the travelling salesman problem, Report 388, Graduate School of Industrial Administration, CMU, 1976.

  • [HK’70] Micheal Held and Richard M. Karp, The Traveling Salesman Problem and Minimum Spanning Trees, Operations Research 18, 1970, 1138–1162.
  • [HK’71] Michael Held and Richard Karp, The Traveling-Salesman Problem and Minimum Spanning Trees: Part II, Mathematical Programming 1, 1971, 6–25.
  • [Christofides’76] Nicos Christofides, Worst-case analysis of a new heuristic for the travelling salesman problem, Report 388, Graduate School of Industrial Administration, CMU, 1976.
  • [FGM’82] A. M. Frieze, G. Galbiati and M. Maffioli, On the Worst-Case Performance of Some Algorithms for the Asymmetric Traveling Salesman Problem, Networks 12, 1982, 23–39.
  • [Blaser’02] M. Blaser, A New Approximation Algorithm for the Asymmetric TSP with Triangle Inequality, Proceedings of the 14th Annual ACM-SIAM Symposium on Discrete Algorithms, 2002, 638–645.
  • [KLSS’03] H. Kaplan, M. Lewenstein, N. Shafir and M. Sviridenko, Approximation Algorithms for Asymmetric TSP by Decomposing Directed Regular Multidigraphs, Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science, 2003, 56–67.
  • [CGK’04] Moses Charikar, Michel X. Goemans, Howard J. Karloff: On the Integrality Ratio for Asymmetric TSP. FOCS 2004: 101-107
  • [FS’07] Uriel Feige, Mohit Singh: Improved Approximation Ratios for Traveling Salesperson Tours and Paths in Directed Graphs. APPROX-RANDOM 2007: 104-118