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This study is investigating the relationship between emotional intelligence and agility of the workforce in order to determine how indicators of emotional intelligence facilitate the agility at the individual level. Statistical Population of this study includes managers, supervisors and staffs of food companies and the simple random sampling method is used. Data has been analyzed by using hierarchical regression statistical methods and comparing average. The survey results have showed that emotional intelligence factors have an impact on workforce agility. The results also have revealed that factors which are related to interpersonal competence (Self-awareness, self-control and self-motivation) have more effects on the agility than factors which are related to social competence (empathy and relation management) and have a greater role in workforce agility changes. Use of the results of this survey in the selection and training of human resources help to create appropriate environment for changing management and organizations movement to agility.

We study an iterative matrix conditioning algorithm due to Osborne (1960). The goal of the algorithm is to convert a square matrix into a balanced matrix where every row and corresponding column have the same norm. The original algorithm was proposed for balancing rows and columns in the L2 norm, and it works by iterating over balancing a row-column pair in fixed round-robin order. Variants of the algorithm for other norms have been heavily studied and are implemented as standard preconditioners in many numerical linear algebra packages. Recently, Schulman and Sinclair (2015), in a first result of its kind for any norm, analyzed the rate of convergence of a variant of Osborne's algorithm that uses the L∞ norm and a different order of choosing row-column pairs. In this paper we study matrix balancing in the L1 norm and other Lp norms. We show the following results for any matrix A = (aij) 1. We analyze the iteration for the L1 norm under a greedy order of balancing. We show that it converges to an -balanced matrix in K = O(min{ −2 log w, −1 n 3/2 log(w/ )}) iterations that cost a total of O(m + Kn log n) arithmetic operations over O(n log(w/ ))-bit numbers. Here m is the number of non-zero entries of A, and w = i,j |aij|/amin with amin = min{|aij| : aij = 0}. 2. We show that the original round-robin implementation converges to an -balanced matrix in O( −2 n 2 log w) iterations totaling O( −2 mn log w) arithmetic operations over O(n log(w/ ))-bit numbers. 3. We show that a random implementation of the iteration converges to an -balanced matrix in O( −2 log w) iterations using O(m + −2 n log w) arithmetic operations over O(log(wn/ ))-bit numbers.4. We demonstrate a lower bound of Ω(1/ √ ) on the convergence rate of any implementation of the iteration. 5. We observe, through a known trivial reduction, that our results for L1 balancing apply to any Lp norm for all finite p, at the cost of increasing the number of iterations by only a factor of p.

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