Hill-climbing optimization

EXAMPLE OF HILL CLIMBING OPTIMIZATION TO MAXIMIZE PURCHASE Best Formulation (Viz Highest Purchase) At Each Iteration... 

On the other hand, the optimal control problem with a discretized control profile can be treated as a nonlinear program. The earliest studies come under the heading of control vector parameterization (Rosenbrock and Storey, 1966), with a representation of U t) as a polynomial or piecewise constant function. Here the mode is solved repeatedly in an inner loop while parameters representing V t) are updated on the outside. While hill climbing algorithms were used initially, recent efficient and sophisticated optimization methods require techniques for accurate gradient calculation from the DAE model. 

Fitting model predictions to experimental observations can be performed in the Laplace, Fourier or time domains with optimal parameter choices often being made using weighted residuals techniques. James et al. [71] review and compare least squares, stochastic and hill-climbing methods for evaluating parameters and Froment and Bischoff [16] summarise some of the more common methods and warn that ordinary moments matching-techniques appear to be less reliable than alternative procedures. References 72 and 73 are studies of the errors associated with a selection of parameter extraction routines. 

For the optimization of, for instance, a tablet formulation, two strategies are available a sequential or a simultaneous approach. The sequential approach consists of a series of measurements where each new measurement is performed after the response of the previous one is knovm. The new experiment is planned according to a direction in the search space that looks promising with respect to the quality criterion which has to be optimized. Such a strategy is also called a hill-climbing method. The Simplex method is a well known example of such a strategy. Textbooks are available that describe the Simplex methods [20]. 

One can consider product optimization as a type of "hill climbing." The product models in Table 4 each represent a hill or a surface, with one hill or surface for each attribute. 

Now we can visualize evolutionary optimization as a hill-climbing process on a landscape that is given by an extremely simple potential [Eqn. (11.15)]. This potential, an ( — 1 )-dimensional hyperplane in n-dimensional space, seems to be a trivial function at first glance. It is linear and hence has no maxima, minima, or saddle points. However, as with every chemical reaction, evolutionary optimization is confined to the cone of nonnegative concentration restricts the physically accessible domain of relative concentrations to the unit simplex (xj > 0, X2 > 0,..., x > 0 Z x = 1). The unit simplex intersects the (n — 1 )-dimensional hyperplane of the potential on a simplex (a three-dimensional example is shown in Figure 4). Selection in the error-free scenario approaches a corner of this simplex, and the stationary state corresponds to a corner equilibrium, as such an optimum on the intersection of a restricted domain with a potential surface is commonly called in theoretical economics. 

The sequential simplex method of optimization was proposed by Nelder and Mead. With a number of improvements and enhancements the simplex method has found great utility in real situations in analytical laboratory experiments and process control situations. The simplex method is a hill-climbing method that seeks to climb the response surface depending on the features of the response surface in its immediate neighborhood. Only one new experiment is done for each step in the optimization sequence, and the location of this new experiment on the response surface is completely determined by the previous few experiments. The method of Nelder and Mead using a variable-size simplex is the most commonly used. A complete description of how the simplex method works is beyond the scope of this review, since the information is contained elsewhere.Many applications of simplex optimization have appeared, and a few examples follow. 

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