Optimization Models
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1. Introduction
1.1. Overview
1.2. Optimization Models
2. Linear Algebra
2.1. Vectors
2.2. Matrices
2.3. Linear Equations
2.4.  Least-Squares
2.5. Eigenvalues
2.6  Singular Values
3. Convex Models
3.1. Convexity
3.2. LP and QP
3.3. SOCP
3.4. Robust LP
3.5. GP
3.6. SDP
3.7. Non-Convex Models
4. Duality
4.1. Weak Duality
4.2. Strong Duality
4.3.  Applications
5. Case Studies
5.1. Senate Voting
5.2. Antenna Arrays
5.3. Localization
5.4. Circuit Design

Nullspace of a transpose incidence matrix

Recall the definition of the arc-node incidence matrix of a network.

By definition, the arc-node incidence matrix A, we have mathbf{1}^T A= 0, where mathbf{1} is the vector of ones (in mathbf{R}^m). Hence mathbf{1} is in the nullspace of A^T.

See also: Rank properties of the arc-node incidence matrix.