PDE Discretization techniques Practical, finite element implementations in MATLAB.
Krylov Space methods MATLAB Project on variants of the GMRES method.
My thesis is still in press, but you can contact me to receive a copy. A second-order globally convergent direct-search method and its worst-case complexity S.
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While there has been some debate in the industry as to the nature of financial modeling—whether it is a tradecraft, such as welding, or a science—the task of financial modeling has been gaining acceptance and rigor over the years.
Typically, financial modeling is understood to mean an exercise in either asset pricing or corporate finance, of a quantitative nature.
In other words, financial modelling is about translating a set of hypotheses about the behavior of markets or agents into numerical predictions; for example, a firm's decisions about investments (the firm will invest 20% of assets), or investment returns In corporate finance and the accounting profession, financial modeling often involves financial statement forecasting.
This usually entails the preparation of detailed company-specific models used for decision making purposes as to the nature of these models: firstly, as they are built around financial statements, calculations and outputs are monthly, quarterly or annual; secondly, the inputs take the form of “assumptions”, where the analyst specifies the values that will apply in each period for external / global variables (exchange rates, tax percentage, etc.…; may be thought of as the model parameters), and for internal / company specific variables (wages, unit costs, etc.…).
resonance frequency or mass) as a result of modifying a parameter value.
The coefficients obtained for all combinations of responses and parameters are stored in a sensitivity matrix.
Correspondingly, both characteristics are reflected (at least implicitly) in the mathematical form of these models: firstly, the models are in discrete time; secondly, they are deterministic.
For discussion of the issues that may arise, see below; for discussion as to more sophisticated approaches sometimes employed, see Corporate finance# Quantifying uncertainty, and Financial economics #Corporate finance theory.
Sensitivity analysis is a technique that allows an analyst to get a feeling on how structural responses of a model are influenced by modifications of parameters like spring stiffness, material stiffness, geometry etc.
Sensitivity analysis can be used for the following purposes: Sensitivity coefficients quantify the variation of a response value (e.g.
Sensitivity analysis and model updating require that the user selects reference responses and parameters.