Figure 18.1 Linear objective function (a) unconstrained (b) subject to linear inequality constraint, x Sx. |

Linear programming problems involve linear objective function and linear constraints. The LP optimum lies at a vertex of the feasible region, which is the basis of the simplex method. LP can have 0 (infeasible), 1, or infinite (multiple) solutions. The set of all feasible solutions to a linear programming problem is a convex set. Therefore, a linear programming optimum is a global optimum. [Pg.70]

The simplest is the case of linear objective function. Let us consider the case when we intend to minimize the overall cost of measurement. The objective function F is of the form [Pg.442]

Remark 1 Formulation (1) has a linear objective function and linear constraints in and y. It also contains a mixed set of variables (i.e., continuous x and 0 - 1 y variables). Note that if the vector c is zero and the matrix A consists of zero elements then (1) becomes an integer linear programming ILP problem. Similarly, if the vector d is zero and the matrix B has all elements zero, then (1) becomes a linear LP problem. [Pg.96]

The original objective function is approximated with its gradient, whereas the nonlinear constraints are linearized. A lower level BzzConstrainedMini-mization class object with a linear objective function and linear constraints is [Pg.446]

The model seeks to maximize the linear objective function (profit). [Pg.490]

Phase 1 minimizes the following linear objective function, the sum of infeasibilities, sinf [Pg.240]

Let us limit ourselves, at the beginning, to the linear objective function (12.2.2). The detailed discussion of this case is presented in Madron and Veverka (1992). It is shown there that the solution is the optimum one (i.e. with the minimum value of function (12.2.2)), with the exception that there are some unobservable nonrequired quantities present. In the latter case the optimality of the solution is not warranted, even if the method looks like good heuristics. The solution then does not represent a global optimum, but only a good, feasible solution applicable to solution of practical problems. [Pg.452]

The optimization problem in this example comprises a linear objective function and linear constraints, hence linear programming is the best technique for solving it (refer to Chapter 7). [Pg.86]

Even though this is a nonlinear function, it can be linearized as follows Let y denote the number of assemblies made. Then the linear objective function is [Pg.2527]

This model features four equalities, five variables and has linear objective function and constraints. Its solution obtained via GAMS/MINOS is [Pg.277]

Place the problem in an LP format with linear constraint equations and linear objective function. [Pg.2444]

An important class of constrained optimization problems has a linear objective function and linear constraints. The solution of these problems is highly structured and can be obtained rapidly via linear programming (LP). This powerful approach is widely used in RTO applications. [Pg.376]

By linear programming we mean a class of optimization methods able to solve problems with both linear objective function and linear constraints. [Pg.355]

The system shown in Figure El 1.4 may be modeled as linear constraints and combined with a linear objective function. The objective is to minimize the operating cost of the system by choice of steam flow rates and power generated or purchased, subject to the demands and restrictions on the system. The following objective function is the cost to operate the system per hour, namely, the sum of steam produced HPS, purchased power required PP, and excess power EP [Pg.436]

The problem described above is a linear programming problem - that is, an optimization problem with a linear objective function and linear constraints. Here the linear object is quite simple (maximize J42). The linear constraints include both linear equalities (SJ = 0) and inequalities (7, >0) yet both sets of constraints are linear in the sense that they involve no non-linear operations on the unknowns (J). [Pg.226]

The nature of the function / is the next step in problem classification. Many application areas such as finance and management-planning tackle linear or quadratic objective functions. These can be written in vector form, respectively, as /(x) = b x -I- fa and /(x) = x Ax -i- b x + fa where b is a column vector, fa is a scalar, and A is a constant symmetric x matrix (i.e., one whose entries satisfy A,j = Ay, ). The superscripts above refer to a vector transpose thus x y is an inner product. Linear programming problems refer to linear objective functions subject to linear constraints (i.e., a system of linear equations), and quadratic programming problems have quadratic objective functions and linear constraints. [Pg.1144]

The Newton-Raphson approach, being essentially a point-slope method, converges most rapidly for near linear objective functions. Thus it is helpful to note that tends to vary as 1/P and as exp(l/T). For bubble-point-temperature calculation, we can define an objective function [Pg.118]

In Figure 2. the model name is CASCADE, the type of solving procedure is LP since only linear constraints and a linear objective function appear. The objective function variable, Z, is minimized, and consequently, the solve statement is [Pg.953]

Our case-study corresponds to a stochastic version of the boiler/turbo generator system problem presented by Edgar et al. (2001). The system may be modeled as a set of linear constraints and a linear objective function. The demand on the resources are considered as uncertain variables in the problem. The distributions used for the demands are shown [Pg.854]

In optimizing test intervals based on risk (or unavailability) and cost, like in many engineering optimization problems (i.e. design, reliability etc.), one normally faces multi-modal and non-linear objective functions and a variety of both linear and non-linear constraints. This results in a complex and discrete search space with regions of feasible and unfeasible solutions for a discontinuous objective functions that eventually presents local optima. [Pg.632]

Probably the most prominent approach to large-scale metabolic networks is constraint-based flux balance analysis. The steady-state condition Eq. (63) defines a linear equation with respect to the feasible flux distributions v°. Formulating a set of constraints and a linear objective function, the properties of the solution space P can be exploredusing standard techniques of linear programming (LP). In this case, the flux balance approach takes the form [Pg.156]

The Outer Approximation OA addresses problems with nonlinear inequalities, and creates sequences of upper and lower bounds as the GBD, but it has the distinct feature of using primal information, that is the solution of the upper bound problems, so as to linearize the objective and constraints around that point. The lower bounds in OA are based upon the accumulation of the linearized objective function and constraints, around the generated primal solution points. [Pg.113]

Chapter 1 presents some examples of the constraints that occur in optimization problems. Constraints are classified as being inequality constraints or equality constraints, and as linear or nonlinear. Chapter 7 described the simplex method for solving problems with linear objective functions subject to linear constraints. This chapter treats more difficult problems involving minimization (or maximization) of a nonlinear objective function subject to linear or nonlinear constraints [Pg.265]

The performed analysis of problems solved by using MEIS has shown the possibilities for their reduction to convex programming (CP) problems in many important cases. Such reduction is often associated with approximation of dependences among variables. There are cases of multivalued solutions to the formulated CP problems, when the linear objective function is parallel to one of the linear part of set D y). Naturally the problems with non-convex objective functions or non-convex attainability sets became irreducible to CP. Non-convexity of the latter can occur at setting kinetic constraints by a system of linear inequalities, p>art of which is specified not for the whole region D (y), but its individual zones. [Pg.50]

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