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Two-dimensional linear inequalities are expressions in two variables of the form: + < +, where the inequalities may either be strict or not. The solution set of such an inequality can be graphically represented by a half-plane (all the points on one "side" of a fixed line) in the Euclidean plane. [2]
One particularly useful inequality to analyze homomorphism densities is the Cauchy–Schwarz inequality. The effect of applying the Cauchy-Schwarz inequality is "folding" the graph over a line of symmetry to relate it to a smaller graph. This allows for the reduction of densities of large but symmetric graphs to that of smaller graphs.
The graph of y = ln x. Any monotonically increasing function, by its definition, [9] may be applied to both sides of an inequality without breaking the inequality relation (provided that both expressions are in the domain of that function). However, applying a monotonically decreasing function to both sides of an inequality means the inequality ...
A facet of a polytope is the set of its points which satisfy an essential defining inequality of the polytope with equality. If the polytope is d-dimensional, then its facets are (d − 1)-dimensional. For any graph G, the facets of MP(G) are given by the following inequalities: [1]: 275–279 x ≥ 0 E
Sidorenko's property is equivalent to the following reformulation: For all graphs , if has vertices and an average degree of , then (,) | |.. This is equivalent because the number of homomorphisms from to is twice the number of edges in , and the inequality only needs to be checked when is a graph as previously mentioned.
In artificial intelligence and operations research, constraint satisfaction is the process of finding a solution through a set of constraints that impose conditions that the variables must satisfy. [1] A solution is therefore an assignment of values to the variables that satisfies all constraints—that is, a point in the feasible region.
This holds both for the case of Lie groups and for the Cayley graph of a finitely generated group. A theorem of Varopoulos connects the isoperimetric dimension of a graph to the rate of escape of random walk on the graph. The result states Varopoulos' theorem: If G is a graph satisfying a d-dimensional isoperimetric inequality then
In mathematical optimization and computer science, a feasible region, feasible set, or solution space is the set of all possible points (sets of values of the choice variables) of an optimization problem that satisfy the problem's constraints, potentially including inequalities, equalities, and integer constraints. [1]