In this paper, we consider two problems which can be posed as spectral radius minimization problems. Firstly, we consider the fastest average agreement problem on multi-agent networks adopting a linear information exchange protocol. Mathematically, this problem can be cast as finding an optimal W is an element of R-nxn such that x(k + 1) = Wx(k), W1 = 1, 1(T)W = 1(T) and W is an element of s(E). Here, x(k) is an element of R-n is the value possessed by the agents at the kth time step, 1 is an element of R-n is an all-one vector and s(E) is the set of real matrices in R-nxn with zeros at the same positions specified by a network graph g(V, E), where V is the set of agents and 9 is the set of communication links between agents. The optimal W is such that the spectral radius rho(W - 11(T)/n) is minimized. To this end, we consider two numerical solution schemes: one using the qth-order spectral norm (2-norm) minimization (q-SNM) and the other gradient sampling (GS), inspired by the methods proposed in [Burke, J., Lewis, A., & Overton, M. (2002). Two numerical methods for optimizing matrix stability. Linear Algebra and its Applications, 351-352, 117-145; Xiao, L., & Boyd, S. (2004). Fast linear iterations for distributed averaging. Systems & Control Letters, 53(1), 65-78]. In this context, we theoretically show that when E is symmetric, i.e. no information flow from the ith to the jth agent implies no information flow from the jth to the ith agent, the solution W-s((1)) from the 1-SNM method can be chosen to be symmetric and W-s((1)) is a local minimum of the function rho(W - 11(T)/n). Numerically, we show that the q-SNM method performs much better than the GS method when 9 is not symmetric. Secondly, we consider the famous static output feedback stabilization problem, which is considered to be a hard problem (some think NP-hard): for a given linear system (A, B, C), find a stabilizing control gain K such that all the real parts of the eigenvalues of A + BKC are strictly negative. In spite of its computational complexity, we show numerically that q-SNM successfully yields stabilizing controllers for several benchmark problems with little effort. (C) 2009 Elsevier Ltd. All rights reserved.