Solve the system of equalities and inequalities

down vote

Backsubstitution is an option often ignored when people use Reduce. In the op’s case it gives:

Reduce[a == b + c && a >= 2 && b <= 10 && c == 5, {a, b, c}, Backsubstitution -> True]
2 <= a <= 15 && b == -5 + a && c == 5

via equation solving – Solve the system of equalities and inequalities – Mathematica Stack Exchange

mathematica graphing a 2 by 2 matrix as an object

is there a possibility to “transform” a matrix (inheriting the input-output “weights”) into a graph object inheriting the matrix relations?

you can convert it to an edge-weighted graph using WeightedAdjacencyGraph. This will give you a complete graph (a Graph expression) in which each vertex is also connected to itself. I am not sure how much sense it makes treat this matrix as a graph, given the full connectivity.

Since you have a fully connected graph, it does not matter in your case, but be aware that WeightedAdjacencyGraph represents missing edges with Infinity, not with zero. This is somewhat annoying because it is inconsistent with WeightedAdjacencyMatrix, which uses 0. While Infinity does make sense in some (not all) applications, it is much more common to see data that uses 0.

 

 

 

via [✓] Convert matrix into “Graph object”? – Online Technical Discussion Groups—Wolfram Community

SliceDensityPlot3D—Wolfram Language Documentation

SliceDensityPlot3D[f, surf, {x, xmin, xmax}, {y, ymin, ymax}, {z, zmin, zmax}] generates a density plot of f over the slice surface surf as a function of x, y, and z. SliceDensityPlot3D[f, surf, {x, y, z} \[Element] reg] restricts the surface to be within region reg. SliceDensityPlot3D[f, {surf1, surf2, …}, …] generates density plots over several slices.

Source: SliceDensityPlot3D—Wolfram Language Documentation

Geometry of Linear Transformations of the Plane – HMC Calculus Tutorial

If V=R2 and W=R2, then T:R2R2 is a linear transformation if and only if there exists a 22 matrix A such that T(v)=Av for all vR2. Matrix A is called the standard matrix for T. The columns of A are T01 and T10 , respectively. Since each linear transformation of the plane has a unique standard matrix, we will identify linear transformations of the plane by their standard matrices. It can be shown that if A is invertible, then the linear transformation defined by A maps parollelograms to parallelograms. We will often illustrate the action of a linear transformation T:R2R2 by looking at the image of a unit square under T

Source: Geometry of Linear Transformations of the Plane – HMC Calculus Tutorial

Affine transformation – Wikipedia

the image T(P) of any point P is determined by noting that T(A) = A′, T applied to the line segment AB is A′B′, T applied to the line segment AC is A′C′, and T respects scalar multiples of vectors based at A. [If A, E, F are collinear then the ratio length(AF)/length(AE) is equal to length(A′F′)/length(A′E′).] Geometrically T transforms the grid based on ABCD to that based in A′B′C′D′.Affine transformations do not respect lengths or angles; they multiply area by a constant factor

Source: Affine transformation – Wikipedia

Ehrhart polynomials pick’s theorem – Google Search

Search ResultsIn mathematics, an integral polytope has an associated Ehrhart polynomial that encodes the relationship between the volume of a polytope and the number of integer points the polytope contains. The theory of Ehrhart polynomials can be seen as a higher-dimensional generalization of Pick’s theorem in the Euclidean plane.

Source: Ehrhart polynomials pick’s theorem – Google Search