Past exam of the mathematics course of the University of Cambridge / 2001 / iii / Paper 31 / 3 / Solution

A reduction must encode primal solutions and infeasibility or unboundedness information; merely declaring a known optimum or subtracting an unknown optimum from the objective function is not a construction. The following homogeneous self-dual embedding of a linear program works without assuming the input problem has an optimum.

First remove linearly dependent equality rows by Gaussian elimination. If an eliminated row has an inconsistent right-hand side, the original problem is already certified infeasible. Return the one-variable special-form program $\min 0$ with $A^{\ast }=0$, $y=1$, and retain that certificate; no original mathematical optimization remains. Otherwise the remaining matrix has full row rank; write it as $A$ again. Put $e=1\in {\mathbb{R}}^n$ andUse nonnegative variables $x,s\in {\mathbb{R}}^n$ and $\tau ,\kappa ,\theta ,\rho \in \mathbb{R}$, with a temporary unrestricted multiplier $\lambda \in {\mathbb{R}}^m$. Impose the homogeneous linear systemAll coefficients are computable from the input. The temporary free multiplier can be eliminated without introducing cancelling positive/negative variables. DefineReplace the second equation by $(I-A^{T}H)q=0$, and substitute $Hq$ in the other equations. Because $A$ has full row rank, $A^T\lambda =q$ is equivalent to these conditions. For no remaining equality rows, simply omit $\lambda$ and its empty equations.

Let $y=(x,s,\tau ,\kappa ,\theta ,\rho )$ and $n^{\ast }=2n+4$. Collect these substituted equations into $A^{\ast }y=0$, impose $1^{T}y=1$, and let $c^{\ast }$ select the coordinate $\theta$. The resulting Karmarkar standard form isThe next two parts prove its known positive starting point and zero optimal value. Notice that normalization is legitimate for any nonzero nonnegative embedding vector because all the equations are homogeneous; it does not require a prior bound on the original optimizer.