nonlinear programming


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nonlinear programming

[′nän‚lin·ē·ər ′prō‚gram·iŋ]
(mathematics)
A branch of applied mathematics concerned with finding the maximum or minimum of a function of several variables, when the variables are constrained to yield values of other functions lying in a certain range, and either the function to be maximized or minimized, or at least one of the functions whose value is constrained, is nonlinear.

Nonlinear programming

The area of applied mathematics and operations research concerned with finding the largest or smallest value of a function subject to constraints or restrictions on the variables of the function. Nonlinear programming is sometimes referred to as nonlinear optimization.

A useful example concerns a power plant that uses the water from a reservoir to cool the plant. The heated water is then piped into a lake. For efficiency, the plant should be run at the highest possible temperature consistent with safety considerations, but there are also limits on the amount of water that can be pumped through the plant, and there are ecological constraints on how much the lake temperature can be raised. The optimization problem is to maximize the temperature of the plant subject to the safety constraints, the limit on the rate at which water can be pumped into the plant, and the bound on the increase in lake temperature.

The nonlinear programming problem refers specifically to the situation in which the function to be minimized or maximized, called the objective function, and the functions that describe the constraints are nonlinear functions. Typically, the variables are continuous; this article is restricted to this case.

Researchers in nonlinear programming consider both the theoretical and practical aspects of these problems. Theoretical issues include the study of algebraic and geometric conditions that characterize a solution, as well as general notions of convexity that determine the existence and uniqueness of solutions. Among the practical questions that are addressed are the mathematical formulation of a specific problem and the development and analysis of algorithms for finding the solution of such problems.

The general nonlinear programming problem can be stated as that of minimizing a scalar-valued objective function f(x) over all vectors x satisfying a set of constraints. The constraints are in the form of general nonlinear equations and inequalities. Mathematically, the nonlinear programming problem may be expressed as below,

where x = (x1, x2, …, xn) are the variables of the problem, f is the objective function, gi( x ) are the inequality constraints, and hj( x ) are the equality constraints. This formulation is general in that the problem of maximizing f( x ) is equivalent to minimizing -f( x ) and a constraint gi( x ) ≥ 0 is equivalent to the constraint -gi( x ) ≤ 0.

Since general nonlinear equations cannot be solved in closed form, iterative methods must be used. Such methods generate a sequence of approximations, or iterates, that will converge to a solution under specified conditions. Newton's method is one of the best-known methods and is the basis for many of the fastest methods for solving the nonlinear programming problem.

References in periodicals archive ?
1) Take any non-linear constraint from the nonlinear programming problem.
Practical Methods for Optimal Control Using Nonlinear Programming.
This primary text for an introductory course on linear and nonlinear programming can be understood by undergraduate students who have completed both a standard single-variable calculus sequence and an introductory linear algebra course.
These parameters present variables in a nonlinear programming model.
The details of casting the design problem introduced in the previous section in such a way that it may be solved using standard nonlinear programming algorithms are presented in this section.
What I like best about the book is the use of simple, but actual research problems to illustrate the uses of linear and nonlinear programming.
Bector: On various duality theorems for second-order duality in nonlinear programming, CCERO, 28(1986), 283-292.
Practical methods for optimal control and estimation using nonlinear programming, 2d ed.
Coverage includes the basics of management science, foundational models, linear programming, duality, sensitivity analysis, computer solutions of linear programming, integer and zero-one programming, goal programming, transportation, network models, and nonlinear programming, along with such techniques as PERT and CPM project planning, decision and game theory, the analytical process, inventory models, queuing models, dynamic programming and simulation, forecasting, Markov analysis, and relations to information systems.
The eleven papers discuss extremal problems for convex polygons, finite algorithms for global minimization of separable concave programs, trust-tech-based global optimization methodology for nonlinear programming, global optimization issues in parametric programming and control, optimization in biomedical research, an overview of advances in global optimization during 2003-2008, software development for global optimization, connected dominating sets in hypergraphs, and global optimization of pessimistic bi-level problems.
These derivatives are employed to speed up the sequential nonlinear programming (SNLP) optimizer included with the Optimetrics[TM] add-on program.
The volume details, in three parts, fundamentals and methods for analytical and numerical modeling in the field and optimization techniques (encompassing statistical, multiobjective, and dynamic optimization techniques and mixed integer linear and nonlinear programming methodologies).

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