Nonlinear programming problem

MJNOS5.4. A package available from Stanford Research Institute (affiliated with Stanford University). This package is the state of the ari for mildly nonlinear programming problems. 

All of these methods have been utilized to solve nonlinear programming problems in the field of chemical engineering design and operations (Lasdon and Waren, Oper. Res., 5, 34, 1980). Nonlinear programming is receiving increased usage in the area of real-time optimization. 

The synthesis of operating procedures for continuous chemical plants can be represented as a mixed-integer nonlinear programming problem, and it has been addressed as such by other researchers. In this chapter we have attempted to present a unifying theoretical framework, which ad-... 

Here the B-spline Bim(zf, Xj) is the ith B-spline basis function on the extended partition Xj (which contains locations of the knots in the Zj direction), and is a coefficient. We use cubic splines and sufficient numbers of uniformly spaced knots so that the estimation problem is not affected by the partition. The estimation problem now involves determining the set of B-spline coefficients that minimizes Eq. (4.1.26), subject to the state equations [Eqs. (4.1.24 and 4.1.25)], for a suitable value of the regularization parameter. At this point, the minimization problem corresponds to a nonlinear programming problem. 

Another method for solving nonlinear programming problems is based on quadratic programming (QP)1. Quadratic programming is an optimization procedure that minimizes a quadratic objective function subject to linear inequality or equality (or both types) of constraints. For example, a quadratic function of two variables x and X2 would be of the general form ... 

Stochastic optimization methods described previously, such as simulated annealing, can also be used to solve the general nonlinear programming problem. These have the advantage that the search is sometimes allowed to move uphill in a minimization problem, rather than always searching for a downhill move. Or, in a maximization problem, the search is sometimes allowed to move downhill, rather than always searching for an uphill move. In this way, the technique is less vulnerable to the problems associated with local optima. 

The SQP strategy applies the equivalent of a Newton step to the KKT conditions of the nonlinear programming problem, and this leads to a fast rate of convergence. By adding slack variables s, the first-order KKT conditions can be rewritten as... 

Optimization of a distributed parameter system can be posed in various ways. An example is a packed, tubular reactor with radial diffusion. Assume a single reversible reaction takes place. To set up the problem as a nonlinear programming problem, write the appropriate balances (constraints) including initial and boundary conditions using the following notation ... 

In summary, the optimum of a nonlinear programming problem is, in general, not at an extreme point of the feasible region and may not even be on the boundary. Also, the problem may have local optima distinct from the global optimum. These properties are direct consequences of nonlinearity. A class of nonlinear problems can be defined, however, that are guaranteed to be free of distinct local optima. They are called convex programming problems and are considered in the following section. 

An important result in mathematical programming evolves from the concept of convexity. For the nonlinear programming problem called the convex programming problem... 

For example, it is usually impossible to prove that a given algorithm will find the global minimum of a nonlinear programming problem unless the problem is convex. For nonconvex problems, however, many such algorithms find at least a local minimum. Convexity thus plays a role much like that of linearity in the study of dynamic systems. For example, many results derived from linear theory are used in the design of nonlinear control systems. 

Sketch the objective function and constraints of the following nonlinear programming problems. 

Shade the feasible region of the nonlinear programming problems of Problem 4.7. Is x = [1 l]r an interior, boundary, or exterior point in these problems ... 

In nonlinear programming problems, optimal solutions need not occur at vertices and can occur at points with positive degrees of freedom. It is possible to have no active constraints at a solution, for example in unconstrained problems. We consider nonlinear problems with constraints in Chapter 8. 

Consequently, in this chapter we will discuss five major approaches for solving nonlinear programming problems with constraints ... 

The Kuhn-Tucker conditions are predicated on this fact At any local constrained optimum, no (small) allowable change in the problem variables can improve the value bf the objective function. To illustrate this statement, consider the nonlinear programming problem ... 

Successive linear programming (SLP) methods solve a sequence of linear programming approximations to a nonlinear programming problem. Recall that if g,(x) is a nonlinear function and x° is the initial value for x, then the first two terms in the Taylor series expansion of gt(x) around x° are... 

Successive quadratic programming (SQP) methods solve a sequence of quadratic programming approximations to a nonlinear programming problem. Quadratic programs (QPs) have a quadratic objective function and linear constraints, and there exist efficient procedures for solving them see Section 8.3. As in SLP, the linear constraints are linearizations of the actual constraints about the selected point. The objective is a quadratic approximation to the Lagrangian function, and the algorithm is simply Newton s method applied to the KTC of the problem. 

Explain in no more than three sentences how an initially feasible starting point can be obtained in solving a nonlinear programming problem. Demonstrate on the problem... 

The nonlinear programming problem is Find 5, and Pijk that will... 

The nonlinear programming problem based on objective function (/), model equations (b)-(g), and inequality constraints (was solved using the generalized reduced gradient method presented in Chapter 8. See Setalvad and coworkers (1989) for details on the parameter values used in the optimization calculations, the results of which are presented here. 

Although, as explained in Chapter 9, many optimization problems can be naturally formulated as mixed-integer programming problems, in this chapter we will consider only steady-state nonlinear programming problems in which the variables are continuous. In some cases it may be feasible to use binary variables (on-off) to include or exclude specific stream flows, alternative flowsheet topography, or different parameters. In the economic evaluation of processes, in design, or in control, usually only a few (5-50) variables are decision, or independent, variables amid a multitude of dependent variables (hundreds or thousands). The number of dependent variables in principle (but not necessarily in practice) is equivalent to the number of independent equality constraints plus the active inequality constraints in a process. The number of independent (decision) variables comprises the remaining set of variables whose values are unknown. Introduction into the model of a specification of the value of a variable, such as T = 400°C, is equivalent to the solution of an independent equation and reduces the total number of variables whose values are unknown by one. 

As a consequence, the gradient of the objective function and the Jacobian matrix of the constraints in the nonlinear programming problem cannot be determined analytically. Finite difference substitutes as discussed in Section 8.10 had to be used. To be conservative, substitutes for derivatives were computed as suggested by Curtis and Reid (1974). They estimated the ratio /x of the truncation error to the roundoff error in the central difference formula... 

The sufficient conditions for obtaining a global solution of the nonlinear programming problem are that both the objective function and the constraint set be convex. If these conditions are not satisfied, there is no guarantee that the local optima will be the global optima. 

As pointed out by Kim et al. (1990), the difference between this algorithm and that of Patino-Leal is that the successive linearization solution is replaced with the nonlinear programming problem in Eq. (9.23). The nested NLP is solved as a set of decoupled NLPs, and the size of the largest optimization problem to be solved is reduced to the order of n. 

If any of the functions f x),h(x),g(x) is nonlinear, then the formulation (3.3) is called a constrained nonlinear programming problem. The functions f(x),h(x),g(x) can take any form of nonlinearity, and we will assume that they satisfy continuity and differentiability requirements. 

Constrained nonlinear programming problems abound in a very large number of science and engineering areas such as chemical process design, synthesis and control facility location network design electronic circuit design and thermodynamics of atomic/molecular clusters. 

Illustration 3.2.2 Consider the following two-variable constrained nonlinear programming problem in the form (3.3) ... 

In this section we present, under the assumption of differentiability, the first-order necessary optimality conditions for the constrained nonlinear programming problem (3.3) as well as the corresponding geometric necessary optimality conditions. 

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