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The consequences of this ill conditioning are threefold First, it indicates that the putative solution is in a at region of the parameter space, so that some parameter values can change by large amounts while the objective function changes hardly at all This situation can make it dif cult to locate and to verify the minimum of a NLS problem (or the maximum of a NML problem) Second, this ill conditioning leads to serious cumulated rounding error and a loss of accuracy in computed numbers As a general rule, when solving a linear system, one digit of accuracy is lost for every power of 10 in the condition number (Judd 1998, p 68) A PC has 16 digits If the condition number of the Hessian is on the order of 109 , the coef cients will be accurate to no more than seven digits.

[HMS98]. Moreover, without proper guidance it is time-consuming and error-prone to navigate through all these design alternatives. The remainder of this section illustrates how eight patterns in this book have been applied to produce a flexible and efficient Web server called JAWS [HPS99]. JAWS is both a Web server and a framework from which other types of servers can be built [HS98]. We selected JAWS as our example application for four reasons: It is a production-quality Web server [ENTERA00] that is representative of challenges that arise when developing concurrent and networked software. JAWS' throughput and scalability are high and its latency and jitter are low [HPS99] [HMS98], demonstrating that pattern- and framework-oriented software architectures can be efficient. The JAWS framework itself is developed using the ACE framework [Sch97], which provides object-oriented implementations of most patterns in this book. [3] ACE and JAWS are open-source, so you can see first-hand how patterns are applied to avoid rediscovering and reinventing solutions to concurrent and networked software design problems.

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This is a negatively phrased question. The interviewers are implying that you are running away from your current or last career rather than looking to change into a new career for positive reasons. Avoid going into detail about the reasons you are disillusioned with your current or last career. Focus instead on the positive reasons that attract you to your new career. No one likes a moaner!

In the proposed heuristic, the idea is to remove, at each iteration, a token in a place by meeting two fundamental objectives: reduce the value of the criterion f(m0), that is the cost of the resources, as much as possible; increase as weakly as possible the average cycle time, i.e. decrease the productivity by as little as possible. As recalled in section 18.3.3, one way to compute analytically the average cycle time of a WMG (in the mono-server case) consists of building the equivalent MG. This depends on the initial marking and not only on the structure. Therefore, it is necessary to build as many extensions as places of the WMG to determine the adequate place in which to remove a token, at each iteration. In this case, the computation time can be important and it is difficult to solve large problems. Consequently, the simulation is used to determine the average cycle time of a WMG. To be able to solve the marking optimization problem by limiting the number of simulations (that is by limiting computation time), the information obtained during the simulation allows us to estimate the average cycle time and to determine the best place. In particular, for each place p P, the following quantity is computed:

The basic Cholesky procedure is a one-pass algorithm that generates two output matrices which can then be combined for the desired square root matrix. The algorithm moves down the main diagonal of the input matrix determining diagonal values of D and triangular values of L from the current column of C and previously calculated components of L and C. Thus the procedure is necessarily sensitive to values in the original matrix and previously calculated values in the D and L matrices. There are k stages in the algorithm corresponding to the k-dimensionality of the input matrix. The j th step (1 j k) is characterized by two operations:

Companies often assume that their industry reaches a commoditization stage, with price the only differentiator, naturally, after their successes and the appearance of competitors. Then discounting is the way to reach customers attention. Ours is a mature, tired market they will claim. It might be the truth, but it represents executive laziness, not customer tiredness. Managers become complacent and tired and prefer not to take the big risks associated with innovation. Cutting costs and reducing prices seem to be easier, more predictable tasks. It is also the fast lane to extinction, but most executives prefer to ignore this fact. The commoditization rut is a state of mind, one in which industry executives are not willing to take their business to the next level or are too afraid or embarrassed to ask for help. For every mature market story I hear, I think about JetBlue, Amazon.com, and Dell, all of which gave their industry a kick, while changing the rules and creating a great new business in someone else s mature market.

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