matrix

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Fig. 58 Matrix. The matrix structure.

matrix

an ORGANIZATION structure in which individuals report to managers in more than one DEPARTMENT or function. The simple CHAIN OF COMMAND found in the classic BUREAUCRACY is replaced by (potentially) a multiplicity of reporting relationships. This type of structure may characterize part of the organization – for project team management for instance, where a project manager assumes authority over team members drawn from a number of departments – or it may extend to the entire organization. See Fig. 58.

There is no standard form of matrix. Managers may have equal formal authority over subordinates or alternatively one of these may have primary authority with the others, assuming authority on particular issues, as in the dotted-line relationship (see ORGANIZATION CHART). The benefits of matrix organization are said to be that it facilitates interdepartmental coordination during innovation, and, by weakening departmental boundaries, encourages greater flexibility and creativity. However, many organizations that have assumed this form have found that the absence of clarity in lines of authority and responsibility can lead to inertia and conflict. See FUNCTIONAL STRUCTURE, PRODUCT-BASED STRUCTURE, CRITICAL FUNCTION STRUCTURE, CONCURRENT ENGINEERING.

References in periodicals archive ?
T] represents the noise signal vector, which is assumed to be a zero-mean complex random vector with a covariance matrix of [?
In this section, we first establish benchmark data by covariance matrix and MD.
Multiplying the inverted variance covariance matrix by both sides produces the following:
In practice, modelers restrict the covariance matrix of the innovations to the laws of motion for the time-varying coefficients in order to sharpen inference.
Given the expected excess rate of return vector R - r on n risky securities and the non-singular covariance matrix [OMEGA] between n risky securities rate of returns, the portfolio [omega] in Equation (1) is the unique risky optimal mean-variance efficient within the mean-variance framework if and only if [omega] = [[OMEGA].
Thus, consider the covariance matrix [SIGMA] of dimension 20x20, which is symmetric and positive definite and apply the logarithm function.
To obtain the covariance matrix, univariate models are estimated and standardized residuals are computed, which in turn serve as the basis for calculating linear correlation coefficients.
1] in place of the covariance matrix (14) we can consider the covariance matrix
Array signal processing and TFDs are combined in this model and the conventional data covariance matrix is replaced by STFD matrix based on time-frequency points (t-f points) in subspace estimation methods.
Statistical algorithms such as Capon beamformer [5] generally require several successive snapshots of data to form an estimate of the covariance matrix of interference to recover the desired signal from the noise contaminated measurements.