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Reduced Cost
In linear programming, reduced cost, or opportunity cost, is the amount by which an objective function coefficient would have to improve (so increase for maximization problem, decrease for minimization problem) before it would be possible for a corresponding variable to assume a positive value in the optimal solution. It is the cost for increasing a variable by a small amount, i.e., the first derivative from a certain point on the polyhedron that constrains the problem. When the point is a vertex in the polyhedron, the variable with the most extreme cost, negatively for minimization and positively maximization, is sometimes referred to as the steepest edge. Given a system minimize \mathbf^T\mathbf subject to \mathbf\leq\mathbf, \mathbf\geq 0, the reduced cost vector can be computed as \mathbf - \mathbf^T \mathbf, where \mathbf is the dual cost vector. It follows directly that for a minimization problem, any non-basic variables at their lower bounds with strictly negative reduced ...
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Linear Programming
Linear programming (LP), also called linear optimization, is a method to achieve the best outcome (such as maximum profit or lowest cost) in a mathematical model whose requirements are represented by linear function#As a polynomial function, linear relationships. Linear programming is a special case of mathematical programming (also known as mathematical optimization). More formally, linear programming is a technique for the mathematical optimization, optimization of a linear objective function, subject to linear equality and linear inequality Constraint (mathematics), constraints. Its feasible region is a convex polytope, which is a set defined as the intersection (mathematics), intersection of finitely many Half-space (geometry), half spaces, each of which is defined by a linear inequality. Its objective function is a real number, real-valued affine function, affine (linear) function defined on this polyhedron. A linear programming algorithm finds a point in the polytope where ...
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Objective Function
In mathematical optimization and decision theory, a loss function or cost function (sometimes also called an error function) is a function that maps an event or values of one or more variables onto a real number intuitively representing some "cost" associated with the event. An optimization problem seeks to minimize a loss function. An objective function is either a loss function or its opposite (in specific domains, variously called a reward function, a profit function, a utility function, a fitness function, etc.), in which case it is to be maximized. The loss function could include terms from several levels of the hierarchy. In statistics, typically a loss function is used for parameter estimation, and the event in question is some function of the difference between estimated and true values for an instance of data. The concept, as old as Laplace, was reintroduced in statistics by Abraham Wald in the middle of the 20th century. In the context of economics, for example, this ...
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Polyhedron
In geometry, a polyhedron (plural polyhedra or polyhedrons; ) is a three-dimensional shape with flat polygonal faces, straight edges and sharp corners or vertices. A convex polyhedron is the convex hull of finitely many points, not all on the same plane. Cubes and pyramids are examples of convex polyhedra. A polyhedron is a 3-dimensional example of a polytope, a more general concept in any number of dimensions. Definition Convex polyhedra are well-defined, with several equivalent standard definitions. However, the formal mathematical definition of polyhedra that are not required to be convex has been problematic. Many definitions of "polyhedron" have been given within particular contexts,. some more rigorous than others, and there is not universal agreement over which of these to choose. Some of these definitions exclude shapes that have often been counted as polyhedra (such as the self-crossing polyhedra) or include shapes that are often not considered as valid polyhedr ...
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Basic Variable
BASIC (Beginners' All-purpose Symbolic Instruction Code) is a family of general-purpose, high-level programming languages designed for ease of use. The original version was created by John G. Kemeny and Thomas E. Kurtz at Dartmouth College in 1963. They wanted to enable students in non-scientific fields to use computers. At the time, nearly all computers required writing custom software, which only scientists and mathematicians tended to learn. In addition to the program language, Kemeny and Kurtz developed the Dartmouth Time Sharing System (DTSS), which allowed multiple users to edit and run BASIC programs simultaneously on remote terminals. This general model became very popular on minicomputer systems like the PDP-11 and Data General Nova in the late 1960s and early 1970s. Hewlett-Packard produced an entire computer line for this method of operation, introducing the HP2000 series in the late 1960s and continuing sales into the 1980s. Many early video games trace their histo ...
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Pivot Strategy
Pivot may refer to: *Pivot, the point of rotation in a lever system *More generally, the center point of any rotational system *Pivot joint, a kind of joint between bones in the body *Pivot turn, a dance move Companies *Incitec Pivot, an Australian chemicals and explosives manufacturer *Pivot Legal Society, a legal advocacy organization based in Vancouver, British Columbia *Pivot Wireless, a cell phone service, created by a joint venture between Sprint and multiple cable companies Computing *Apache Pivot, an open-source platform for building applications in Java *Microsoft Live Labs Pivot, a data search application *Morrow Pivot and Morrow Pivot II, early laptop computers *Pivot, an element of the quicksort algorithm *Pivot, now PivotX, a content management system designed for bloggers *Pivot display, a display which can change orientation *Pivot Stickfigure Animator, stick-figure animation software *Pivot table, a data summarization tool in spreadsheets *Pivotal Games, a former ...
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Devex Algorithm
In applied mathematics, the devex algorithm is a pivot rule for the simplex method In mathematical optimization, Dantzig's simplex algorithm (or simplex method) is a popular algorithm for linear programming. The name of the algorithm is derived from the concept of a simplex and was suggested by T. S. Motzkin. Simplices are ... developed by Paula M. J. Harris. It identifies the steepest-edge approximately in its search for the optimal solution.Forrest, John J., and Donald Goldfarb.Steepest-edge simplex algorithms for linear programming" Mathematical programming 57.1–3 (1992): 341–374. References Algorithms {{algorithm-stub ...
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Linear Programming
Linear programming (LP), also called linear optimization, is a method to achieve the best outcome (such as maximum profit or lowest cost) in a mathematical model whose requirements are represented by linear function#As a polynomial function, linear relationships. Linear programming is a special case of mathematical programming (also known as mathematical optimization). More formally, linear programming is a technique for the mathematical optimization, optimization of a linear objective function, subject to linear equality and linear inequality Constraint (mathematics), constraints. Its feasible region is a convex polytope, which is a set defined as the intersection (mathematics), intersection of finitely many Half-space (geometry), half spaces, each of which is defined by a linear inequality. Its objective function is a real number, real-valued affine function, affine (linear) function defined on this polyhedron. A linear programming algorithm finds a point in the polytope where ...
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Shadow Price
A shadow price is the monetary value assigned to an abstract or intangible commodity which is not traded in the marketplace. This often takes the form of an externality. Shadow prices are also known as the recalculation of known market prices in order to account for the presence of distortionary market instruments (e.g. quotas, tariffs, taxes or subsidies). Shadow Prices are the real economic prices given to goods and services after they have been appropriately adjusted by removing distortionary market instruments and incorporating the societal impact of the respective good or service. A shadow price is often calculated based on a group of assumptions and estimates because it lacks reliable data, so it is subjective and somewhat inaccurate. The need for shadow prices arises as a result of “externalities” and the presence of distortionary market instruments. An externality is defined as a cost or benefit incurred by a third party as a result of production or consumption of a g ...
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