Information about Simplex Algorithm
In mathematical optimization theory, the simplex algorithm, created by the American mathematician George Dantzig in 1947, is a popular technique for numerical solution of the linear programming problem. An unrelated, but similarly named method is the Nelder-Mead method or downhill simplex method due to Nelder & Mead (1965) and is a numerical method for optimising many-dimensional unconstrained problems, belonging to the more general class of search algorithms.
In both cases, the method uses the concept of a simplex, which is a polytope of N + 1 vertices in N dimensions: a line segment in one dimension, a triangle in two dimensions, a tetrahedron in three-dimensional space and so forth.
A linear programming problem consists of a collection of linear inequalities on a number of real variables and a given linear functional (on these real variables) which is to be maximized or minimized. Further details are given in the linear programming article.
In geometric terms we are considering a closed convex polytope, P, defined by intersecting a number of half-spaces in n-dimensional Euclidean space; each half-space is the area which lies on one side of a hyperplane. If the objective is to maximise a linear functional L(x), consider the hyperplanes H(c) defined by
; as c increases, these form a parallel family. If the problem is well-posed, we want to find the largest value of c such that H(c) intersects P (if there is no such largest value of c, this isn't a reasonable question for optimization as it stands). In this case we can show that the optimum value of c is attained on the boundary of P. Methods for finding this optimum point on P work in several ways: some attempt to improve a possible point by moving through the interior of P (so-called interior point methods); others start and remain on the boundary searching for an optimum.
The simplex algorithm follows this latter methodology. The idea is to move along the facets of P in search of the optimum, from point to point. Note that, unless the optimum occurs on an edge or face that is parallel to H, the optimum will be unique and occur at a vertex of the polytope. If an optimum is found on an edge or face that is parallel to H, the optimum is not unique and can be obtained at any point on the edge or face. Since the simplex algorithm is concerned only with finding a single optimal point (even if other equally-optimal points exist), it is possible to look solely at moves skirting the edge of a simplex, ignoring the interior. The algorithm specifies how this is to be done.
We start at some vertex of the polytope, and at every iteration we choose an adjacent vertex such that the value of the objective function does not decrease. If no such vertex exists, we have found a solution to the problem. But usually, such an adjacent vertex is nonunique, and a pivot rule must be specified to determine which vertex to pick. Various pivot rules exist.
In 1972, Klee and Minty [1] gave an example of a linear programming problem in which the polytope P is a distortion of an n-dimensional cube. They showed that the simplex method as formulated by Dantzig visits all 2n vertices before arriving at the optimal vertex. This shows that the worst-case complexity of the algorithm is exponential time. Similar examples have been found for other pivot rules. It is an open question if there is a pivot rule with polynomial time worst-case complexity.
Nevertheless, the simplex method is remarkably efficient in practice. Attempts to explain this employ the notion of average complexity or (recently) smoothed complexity.
Other algorithms for solving linear programming problems are described in the linear programming article.
The system is typically underdetermined, since the number of variables exceed the number of equations. The difference between the number of variables and the number of equations gives us the degrees of freedom associated with the problem. Any solution, optimal or not, will therefore include a number of variables of arbitrary value. The simplex algorithm uses zero as this arbitrary value, and the number of variables with value zero equals the degrees of freedom.
Variables of nonzero value are called basic variables, and variables of zero value are called nonbasic variables in the simplex algorithm. [This definition is problematic, since basic variables can also take zero value.]
This form simplifies finding the initial basic feasible solution (BF), since all variables from the standard form can be chosen to be nonbasic (zero), while all new variables introduced in the augmented form are basic (nonzero), since their value can be trivially calculated (
for them, since the augmented problem matrix is diagonal on its right half).
are the coefficients of basic variables in the c-matrix; and B is the columns of 
corresponding to the basic variables.
It is worth noting that B and are the only variables in this equation (except Z and x of course). Everything else is constant throughout the algorithm.
A tetrahedron (plural: tetrahedra) is a polyhedron composed of four triangular faces, three of which meet at each vertex.
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A hyperplane is a concept in geometry. It is a higher-dimensional generalization of the concepts of a line in Euclidean plane geometry and a plane in 3-dimensional Euclidean geometry.
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In both cases, the method uses the concept of a simplex, which is a polytope of N + 1 vertices in N dimensions: a line segment in one dimension, a triangle in two dimensions, a tetrahedron in three-dimensional space and so forth.
Description
)
A linear programming problem consists of a collection of linear inequalities on a number of real variables and a given linear functional (on these real variables) which is to be maximized or minimized. Further details are given in the linear programming article.
In geometric terms we are considering a closed convex polytope, P, defined by intersecting a number of half-spaces in n-dimensional Euclidean space; each half-space is the area which lies on one side of a hyperplane. If the objective is to maximise a linear functional L(x), consider the hyperplanes H(c) defined by
; as c increases, these form a parallel family. If the problem is well-posed, we want to find the largest value of c such that H(c) intersects P (if there is no such largest value of c, this isn't a reasonable question for optimization as it stands). In this case we can show that the optimum value of c is attained on the boundary of P. Methods for finding this optimum point on P work in several ways: some attempt to improve a possible point by moving through the interior of P (so-called interior point methods); others start and remain on the boundary searching for an optimum.
The simplex algorithm follows this latter methodology. The idea is to move along the facets of P in search of the optimum, from point to point. Note that, unless the optimum occurs on an edge or face that is parallel to H, the optimum will be unique and occur at a vertex of the polytope. If an optimum is found on an edge or face that is parallel to H, the optimum is not unique and can be obtained at any point on the edge or face. Since the simplex algorithm is concerned only with finding a single optimal point (even if other equally-optimal points exist), it is possible to look solely at moves skirting the edge of a simplex, ignoring the interior. The algorithm specifies how this is to be done.
We start at some vertex of the polytope, and at every iteration we choose an adjacent vertex such that the value of the objective function does not decrease. If no such vertex exists, we have found a solution to the problem. But usually, such an adjacent vertex is nonunique, and a pivot rule must be specified to determine which vertex to pick. Various pivot rules exist.
In 1972, Klee and Minty [1] gave an example of a linear programming problem in which the polytope P is a distortion of an n-dimensional cube. They showed that the simplex method as formulated by Dantzig visits all 2n vertices before arriving at the optimal vertex. This shows that the worst-case complexity of the algorithm is exponential time. Similar examples have been found for other pivot rules. It is an open question if there is a pivot rule with polynomial time worst-case complexity.
Nevertheless, the simplex method is remarkably efficient in practice. Attempts to explain this employ the notion of average complexity or (recently) smoothed complexity.
Other algorithms for solving linear programming problems are described in the linear programming article.
Problem input
Consider a linear programming problem,- maximize
- subject to
- Maximize Z in:
The system is typically underdetermined, since the number of variables exceed the number of equations. The difference between the number of variables and the number of equations gives us the degrees of freedom associated with the problem. Any solution, optimal or not, will therefore include a number of variables of arbitrary value. The simplex algorithm uses zero as this arbitrary value, and the number of variables with value zero equals the degrees of freedom.
Variables of nonzero value are called basic variables, and variables of zero value are called nonbasic variables in the simplex algorithm. [This definition is problematic, since basic variables can also take zero value.]
This form simplifies finding the initial basic feasible solution (BF), since all variables from the standard form can be chosen to be nonbasic (zero), while all new variables introduced in the augmented form are basic (nonzero), since their value can be trivially calculated (
for them, since the augmented problem matrix is diagonal on its right half).
Revised simplex algorithm
Matrix form of the simplex algorithm
At any iteration of the simplex algorithm, the tableau will be of this form:
are the coefficients of basic variables in the c-matrix; and B is the columns of corresponding to the basic variables.
It is worth noting that B and
are the only variables in this equation (except Z and x of course). Everything else is constant throughout the algorithm.
Algorithm
- Choose an initial BF as shown above
- This implies that B is the identity matrix, and is a zero-vector.
- While nonoptimal solution:
- Determine direction of highest gradient
- : Choose the variable associated with the coefficient in
that has the highest negative magnitude. This nonbasic variable, which we call the entering basic, will be increased to help maximize the problem.
- Determine maximum step length
- : Use the
subequation to determine which basic variable reaches zero first when the entering basic is increased. This variable, which we call the leaving basic then becomes nonbasic. This operation only involves a single division for each basic variable, since the existing basic-variables occur diagonally in the tableau.
- Rewrite problem
- : Modify B and to account for the new basic variables. This will automatically make the tableau diagonal for the existing and new basic variables.
- Check for improvement
- : Repeat procedure until no further improvement is possible, meaning all the coefficients of are positive. Procedure is also terminated if all coefficients are zero, and the algorithm has walked in a circle and revisited a previous state.
References
- Greenberg, Harvey J., Klee-Minty Polytope Shows Exponential Time Complexity of Simplex Method University of Colorado at Denver (1997) PDF download
- Frederick S. Hillier and Gerald J. Lieberman: Introduction to Operations Research, 8th edition. McGraw-Hill. ISBN 0-07-123828-X
- Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein. Introduction to Algorithms, Second Edition. MIT Press and McGraw-Hill, 2001. ISBN 0-262-03293-7. Section 29.3: The simplex algorithm, pp.790–804.
- Hamdy A. Taha: Operations Research: An Introduction, 8th ed., Prentice Hall, 2007. ISBN 0-13-188923-0
Note
1. ^ Greenberg, cites: V. Klee and G.J. Minty. "How Good is the Simplex Algorithm?" In O. Shisha, editor, Inequalities, III, pages 159–175. Academic Press, New York, NY, 1972
See also
- Nelder-Mead method
- Fourier-Motzkin elimination
External links
- An Introduction to Linear Programming and the Simplex Algorithm by Spyros Reveliotis of the Georgia Institute of Technology.
- A step-by-step simplex algorithm solver Solve your own LP problem. Note: this does not work well. Sample problem: minimize z=x1+x2 subject to 2x1+3x2<=6 and -x1+x2<=1 should give 0 as answer. This applet gives the same answer for both maximize and minimize problem.
- Java-based interactive simplex tool hosted by Argonne National Laboratory.
- Simplex Algorithm by Elmer G. Wiens. Demonstrates algorithm in detail, using the simplex tableau.
- Tutorial for The Simplex Method by Stefan Waner, hofstra.edu. Interactive worked example.
- A simple example - step by step by Mazoo's Learning Blog.
- Simplex Method A good tutorial for Simplex Method with examples (also two-phase and M-method).
- PHPSimplex Other good tutorial for Simplex Method with the Two-Phase Simplex and Graphical methods, examples, and a tool to solve Simplex problems online.
- Simplex Method Tool Quick-loading web page that solves Simplex problems
In mathematics, the term optimization, or mathematical programming, refers to the study of problems in which one seeks to minimize or maximize a real function by systematically choosing the values of real or integer variables from within an allowed set.
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mathematician is a person whose primary area of study and research is the field of mathematics.
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Problems in mathematics
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George Bernard Dantzig (8 November 1914 – 13 May 2005) was an American mathematician who introduced the simplex algorithm and is considered the "father of linear programming".
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Numerical analysis is the study of algorithms for the problems of continuous mathematics (as distinguished from discrete mathematics).
One of the earliest mathematical writing is the Babylonian tablet YBC 7289, which gives a sexagesimal numerical approximation of ,
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One of the earliest mathematical writing is the Babylonian tablet YBC 7289, which gives a sexagesimal numerical approximation of ,
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In mathematics, linear programming (LP) problems involve the optimization of a linear objective function, subject to linear equality and inequality constraints.
Put very informally, LP is about trying to get the best outcome (e.g.
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Put very informally, LP is about trying to get the best outcome (e.g.
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Nelder-Mead simplex search over the Rosenbrock banana function (above) and Himmelblau's function (below)
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- See simplex algorithm for the numerical solution of the linear programming problem.
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Numerical analysis is the study of algorithms for the problems of continuous mathematics (as distinguished from discrete mathematics).
One of the earliest mathematical writing is the Babylonian tablet YBC 7289, which gives a sexagesimal numerical approximation of ,
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One of the earliest mathematical writing is the Babylonian tablet YBC 7289, which gives a sexagesimal numerical approximation of ,
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dimension (Latin, "measured out") is a parameter or measurement required to define the characteristics of an object—i.e., length, width, and height or size and shape.
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In computer science, a search algorithm, broadly speaking, is an algorithm that takes a problem as input and returns a solution to the problem, usually after evaluating a number of possible solutions.
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In geometry, a simplex (plural simplexes or simplices) or n-simplex is an n-dimensional analogue of a triangle. Specifically, a simplex is the convex hull of a set of (n
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In geometry polytope means, first, the generalization to any dimension of polygon in two dimensions, polyhedron in three dimensions, and polychoron in four dimensions. Beyond that, the term is used for a variety of related mathematical concepts.
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line segment is a part of a line that is bounded by two end points, which have a finite length, and contains every point on the line between its end points. Examples of line segments include the sides of a triangle or square.
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A triangle is one of the basic shapes of geometry: a polygon with three corners or and three sides or edges which are straight line segments.
In Euclidean geometry any three non-collinear points determine a triangle and a unique plane, i.e.
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In Euclidean geometry any three non-collinear points determine a triangle and a unique plane, i.e.
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For the academic journal, see Tetrahedron (journal).
A tetrahedron (plural: tetrahedra) is a polyhedron composed of four triangular faces, three of which meet at each vertex.
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In mathematics, linear programming (LP) problems involve the optimization of a linear objective function, subject to linear equality and inequality constraints.
Put very informally, LP is about trying to get the best outcome (e.g.
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Put very informally, LP is about trying to get the best outcome (e.g.
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prevew not available
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variable (IPA pronunciation: [ˈvæɹiəbl]) (sometimes called a pronumeral) is a symbolic representation denoting a quantity or expression.
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convex if for every pair of points within the object, every point on the straight line segment that joins them is also within the object. For example, a solid cube is convex, but anything that is hollow or has a dent in it, for example, a crescent shape, is not convex.
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In geometry polytope means, first, the generalization to any dimension of polygon in two dimensions, polyhedron in three dimensions, and polychoron in four dimensions. Beyond that, the term is used for a variety of related mathematical concepts.
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half-space is either of the two parts into which a plane divides the three-dimensional space. More generally, a half-space is either of the two parts into which a hyperplane divides an affine space.
One can have open and closed half-spaces.
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One can have open and closed half-spaces.
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Euclidean space. Most of this article is devoted to developing the modern language necessary for the conceptual leap to higher dimensions.
An essential property of a Euclidean space is its flatness. Other spaces exist in geometry that are not Euclidean.
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An essential property of a Euclidean space is its flatness. Other spaces exist in geometry that are not Euclidean.
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Not to be confused with Hypersonic aircraft.
A hyperplane is a concept in geometry. It is a higher-dimensional generalization of the concepts of a line in Euclidean plane geometry and a plane in 3-dimensional Euclidean geometry.
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In geometry, a simplex (plural simplexes or simplices) or n-simplex is an n-dimensional analogue of a triangle. Specifically, a simplex is the convex hull of a set of (n
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