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An auxiliary conic optimization problem searches for strict feasibility before following a central path. Given $e\in \mathrm{int}K$, minimizing $t$ subject to $Ax-b+te\in K$ and $t\ge -1$ has a strict feasible start for sufficiently large $t$. Any feasible solution with $t<0$ certifies $Ax-b\in \mathrm{int}K$. If the original problem is strictly feasible, a small negative $t$ is feasible. Equality constraints and dual feasibility require their corresponding auxiliary procedures.

An interior-point method solves constrained mathematical optimization problems by maintaining variables in the interiors of their feasible cones or inequality domains. Barrier derivatives are defined there, and damped Newton methods keep subsequent iterates in their domains. A central path gives a family of barrier-regularized optima whose duality gap approaches zero; a conic phase-I problem can supply a strict feasible starting point.

For a logarithmically homogeneous barrier on a proper cone, the convex conjugate with reversed argument defines a barrier on the interior dual cone. The inverse gradient relation makes $y=-\mu \nabla F(s)$ equivalent to $s=-\mu \nabla F_{†}(y)$. This gives a joint primal-dual central path without assuming the barrier is identical on a self-dual cone.

A cone barrier with this scaling law satisfies $⟨\nabla F(s),s⟩=-\nu$ and $\nabla F(ts)=t^{-1}\nabla F(s)$. Therefore a central path with $y=-\mu \nabla F(s)$ has primal-dual gap $\nu \mu$. A product of orthant and Lorentz cone canonical barriers adds their parameters.

Use the nonnegative-pairing dual cone $K^{\ast }=\{y:⟨y,s⟩\ge 0\text{ for all }s\in K\}$. The Lagrangian is $L(x,y)=c^{T}x-y^T(Ax-b)$ for $y\in K^{\ast }$. Its infimum over unrestricted $x$ is finite exactly when $A^{T}y=c$. Therefore the conic dual problem isThe final equality uses the self-dual cone assumption.

For the canonical self-concordant barrier, use its logarithmically homogeneous barrier normalizationAt parameter $\mu >0$, the primal barrier problem isWrite $F_{†}(y)=F^{\ast }(-y)$, the Legendre dual cone barrier. The matching dual barrier problem isDefining the dual barrier this way is valid generally; self-duality of the cone alone does not assert that an arbitrarily selected barrier equals its Legendre dual.

The joint central path characterization isThe primal stationarity equation is $c+\mu A^T\nabla F(s)=0$, exactly the dual feasibility equation after defining $y$. For the dual relation, logarithmic homogeneity gives $\nabla F(s/\mu )=-y$, so $\nabla F_{†}(y)=-s/\mu$. Thus dual stationarity is $-b+\mu \nabla F_{†}(y)+Ax=0$, giving the same primal equation. At these points,

Newton step. At a chosen target parameter $\hat{\mu }>0$, defineLinearizing the three equality conditions gives the central-path Newton systemEliminating the slack and dual directions yieldsThe barrier Hessian is positive definite. Full column rank of $A$ therefore makes $A^{T}HA$ positive definite and the reduced solve unique. Use a damped step with $s+\alpha \Delta s$ and $y+\alpha \Delta y$ remaining interior; a full Newton step need not do so.

At an exact point with parameter $\mu$, choosing $\hat{\mu }<\mu$ gives $r_p=r_d=0$ and $r_c=(\hat{\mu }-\mu )\nabla F(s)$, the usual predictor towards the next path point. Infinitesimally,

The path definition presupposes interior feasibility and attainment of the barrier problems; strict primal-dual feasibility is a standard sufficient setting. The printed full-rank condition by itself is insufficient. For example $K={\mathbb{R}}_{+}^2$, $A=(1,-1{)}^T$, $b=0$ and $c=0$ give the sole primal feasible point $x=0$, with no interior slack at all, despite full column rank. Rank guarantees the Newton solve at an interior point, not existence of the central path.

Introduce the primal slack $s=Ax-b\in K$. The Lagrangian is $L(x,y)=c^{\top }x-⟨y,Ax-b⟩$, with $y\in K^{\ast }=K$. Minimizing over $x$ is finite only when $A^{\top }y=c$. Thus the conic dual problem isThe primal-dual gap at feasible points is $c^{\top }x-b^{\top }y=⟨s,y⟩\ge 0$. Self-duality specifies the multiplier cone; it does not alone guarantee feasible or bounded problems. The central-path discussion assumes primal and dual strict feasibility and a finite optimum. These additional existence conditions are not implied by the given full column rank of $A$.

Let $F$ be the canonical logarithmically homogeneous self-concordant barrier, with parameter $\nu$ and $F(ts)=F(s)-\nu \log t$. For each $\mu >0$, minimizing $c^{\top }x+\mu F(Ax-b)$ defines the primal central path. Its stationarity defines the dual path through $y=-\mu \nabla F(s)\in \mathrm{int}K$. The joint characterization isEuler's identity for logarithmic homogeneity gives $⟨s,\nabla F(s)⟩=-\nu$, so $\boxed{⟨s,y⟩=\nu \mu }$. The gap tends to zero as $\mu \downarrow 0$. If $F_{\ast }(y)={\sup }_{s\in \mathrm{int}K}[-⟨y,s⟩-F(s)]$ is the dual barrier, the equivalent dual relation is $s=-\mu \nabla F_{\ast }(y)$. A self-dual cone does not justify identifying two arbitrary primal and dual barrier functions without this relation.

For a target parameter $\bar{\mu }>0$, form residuals $r_p=Ax-b-s$, $r_d=A^{\top }y-c$ and $r_c=y+\bar{\mu }\nabla F(s)$. Linearization gives the central-path Newton systemFor a strictly feasible primal-dual iterate with $r_p=r_d=0$, elimination reduces it toThe barrier Hessian is positive definite and $A$ has full column rank, so the reduced matrix is positive definite. Alternatively a changing path parameter can be included as an additional linear term $\Delta \mu \nabla F(s)$; the displayed system instead fixes the new target parameter before solving.

This is an interior-point method because iterates stay in the cone interiors where the barrier and its gradient/Hessian are defined. A full Newton step need not do so. Use a fraction-to-boundary or backtracking step: decrease $\alpha >0$ until $s+\alpha \Delta s$ and $y+\alpha \Delta y$ lie in $\mathrm{int}K$, then enforce an appropriate barrier or residual decrease. Such a positive step exists because the current points are interior. Local barrier norms can also certify an interior step via the Dikin ellipsoid.

For a practical starting point, choose $e\in \mathrm{int}K$ and solve a conic phase-I problem, for example minimizing $t$ subject to $Ax-b+te\in K$ and $t\ge -1$. A large positive $t$ with an arbitrary $x$ gives a strictly feasible start for this auxiliary problem. A feasible point with $t<0$ certifies $Ax-b\in \mathrm{int}K$. If the original problem is strictly feasible, a small negative $t$ is feasible, so phase I can find such a certificate. A similar feasibility procedure handles the dual equality and interior. Alternatively an infeasible-start primal-dual method or homogeneous self-dual embedding starts with interior cone variables while allowing nonzero linear residuals, and can report infeasibility rather than presume an interior solution exists.

For a proper cone $K$, suppose a point satisfies $Ax-b\in \mathrm{int}K$ and $p_{\ast }$ is finite. This generalized Slater condition gives strong duality and attainment of the conic dual problem's optimum. It does not by itself assert primal attainment.^[ref]

The two worked examples have strict primal points: $(u,v)=(2,2)$ has slack $(2,2,1)$ in the interior of the nonnegative orthant, and $(t,z)=(2,0)$ has slack $(2,1,0)$ in the interior of the second-order cone. Both optima were already certified by explicit feasible pairs. Slater's condition explains why a matching optimal dual variable can be expected under these hypotheses; checking a supplied zero-gap pair needs only weak duality.

Changing cone geometry preserves the bound mechanism. For a positive semidefinite cone, the dual cone pairing is the Frobenius inner product of two matrices. This connects the same certificate idea to semidefinite programming, while the central path uses strictly interior primal and dual points to approach vanishing gap.

In 1995, Lieven Vandenberghe and Stephen Boyd presented a primal-dual interior-point method for convex optimization with matrix inequalities. Their algorithmic study included exploiting problem structure and allowing incomplete Conjugate gradient method solves within an iteration. This illustrates how the primal-dual viewpoint guides computation as well as checking a final answer.^[ref]