Generalization algorithm

For modelling conformational transitions and nonlinear dynamics of NA a phenomenological approach is often used. This allows one not just to describe a phenomenon but also to understand the relationships between the basic physical properties of the system. There is a general algorithm for modelling in the frame of the phenomenological approach determine the dominant motions of the system in the time interval of the process treated and theti write... 

An analytical solution to a variable-density problem is rarely possible. The following example is an exception that illustrates the solution technique first in analytical form and then in numerical form. It is followed by a description of the general algorithm for solving Equation (3.11) numerically. 

Kondili, E., Pantalides, C.C., and Sargent, R.H.W., A general algorithm for short-term scheduling of batch operations. I. MILP formulation. Comput. Chem. Eng. 17 (2) 211-228 (1993). 

In order to obtain the expression for the components of the vector of instantaneous copolymer composition it is necessary, according to general algorithm, to firstly determine the stationary vector ji of the extended Markov chain with the matrix of transitions (13) which describes the stochastic process of conventional movement along macromolecules with labeled units and then to erase the labels. In this particular case such a procedure reduces to the summation ... 

The perceptional advantages of response contours in illustrating nonlinear blending behavior and the additional information of the experimental boundary locations were incorporated into a generalized algorithm which determines the feasible region on a tricoordinate plot for a normal or pseudocomponent mixture having any number of constrained components. 

Given the dependence of X on p, it is possible resorting to the general algorithm [84] to derive an expression for the fraction of monomeric unit s/c( p) (Eq. 1) involved in molecules with composition , which are formed during... 

Fig. 21-1 describes a general algorithm for the interpretation of liver function tests. 

Kondili, E. Pantelides, C. C. and R. W. H. Sargent. A General Algorithm for Short-term Scheduling of Batch Operations—I. MILP Formulation. Comput Chem Eng 17(2) 211-228(1993). 

An essential concept in multivariate data analysis is the mathematical combination of several variables into a new variable that has a certain desired property (Figure 2.14). In chemometrics such a new variable is often called a latent variable, other names are component or factor. A latent variable can be defined as a formal combination (a mathematical function or a more general algorithm) of the variables a latent variable summarizes the variables in an appropriate way to obtain acertain property. The value of a latent variable is called score. Most often linear latent variables are used given by... 

Nonlinear Hoo control is based on Lyapunov design, but with general algorithms... 

Finally, the general algorithm framework of the infeasible primal-dual path-following Mehrotra-type predictor-corrector interior-point method is the following. 

The solution 0C of max-min problem (16) is the critical point which limits the RI that is, it is the point where the largest inscribed polytope meets the feasible region R. In general, 8C need not correspond to a vertex of the polytope (e.g., for some nonconvex feasible regions R). However, to date no general algorithm has been developed to find nonvertex critical points which limit the RI. 

Different algorithms are required if the HEN resilience problem is nonlinear. Special algorithms were presented for testing the resilience of minimum unit HENs with piecewise constant heat capacities, stream splits, or simultaneous flow rate and temperature uncertainties. A more general algorithm, the active constraint strategy, was also presented which can test the resilience or calculate the flexibility index of a HEN with minimum or more units, stream splits and/or bypasses, and temperature and/or flow rate uncertainties, but with constant heat capacities. 

Assuming that the problem (6.2) has a finite optimal value, Geoffrion (1972) stated the following general algorithm for GBD ... 

In the previous section we discussed the general algorithmic statement of GBD and pointed out (see remark 3) a key assumption made with respect to the calculation of the support functions (y A, p) and (y A, p) from the feasible and infeasible primal problems, respectively. In this section, we will discuss a number of variants of GBD that result from addressing the calculation of the aforementioned support functions either rigorously for special cases or making assumptions that may not provide valid lower bounds in the general case. 

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