Inequality Constrained Minimization
Def Inequality Constrained Minimization convex , twice continuously differentiable.
Prop Strong duality holds and dual optimum is attained for the above problem.
Log Barrier Method
Thrm We can reformulate the inequality constraints via indicator functions:
Def Log Barrier
Misplaced & \text{subject to } \quad & Ax=b \end{aligned} $$ **Def** <i><u> Logarithmic Barrier Function</u></i> For a series of inequality constraints $f_i(x)\leq0$ that are strictly feasible, we define the logarithmic barrier function as: $$ \phi(x)=-\sum_{i=1}^m \log(-f_i(x)), \quad \textbf{dom}f=\{ x\mid f_i(x)<0 \text{ for all } i \} $$ **Prop** Logarithmic barrier function is convex and twice continuously differentiable. **Def** <i><u>Central Path</u></i> For $t > 0$, define $x^\star(t)$ as the solution of $$ \begin{aligned}\text{minimize } \quad & tf_0(x)+\phi(x) \\ \text{subject to } \quad & Ax=b\end{aligned} $$ Assume that $x^\star(t)$ exists and is unique for each $t > 0$, then the central path is $\{ x^{\star}(t) \mid t>0 \}$. **Prop** $x = x^\star(t)$ if there exists a $w$ such that $$ t\nabla f_0(x)+\sum_{i=1}^m \frac{1}{-f_i(x)}\nabla f_i(x)+A^{\mathrm{T}}w=0,\quad Ax=b $$ **Prop** The solution of the central path problem guarantees that $f_0(x^\star(t)) − p^\star ≤ \frac{m}{t}$. ^dgap **Thrm** <i><u>Interpretation via KKT Conditions</u></i> $x = x^\star(t), λ = λ^\star(t), ν = ν^\star(t)$ satisfy 1. Primal constraints: $f_i(x) ≤ 0, i = 1,...,m, Ax = b$ 2. Dual constraints: $λ \succeq 0$ 3. Approximate complementary slackness: $−λ_if_i(x) = \frac{1}{t} , i = 1, \dots , m$ 4. Gradient of Lagrangian with respect to $x$ vanishes:$$ \nabla f_0(x)+\sum_{i=1}^m \lambda_i \nabla f_i(x)+A^T \nu=0 $$ >[!remark] > >Difference with KKT is that condition 3 replaces $λ_if_i(x) = 0$ **Thrm** <i><u>Force Field Interpretation</u></i> Consider a centering problem: $$ \begin{aligned}\text{minimize } \quad & tf_0(x)-\sum_{i=1}^m \log(-f_i(x)) \end{aligned} $$ - $tf_0(x)$ is the potential of force field $F_0(x) = −t∇f_0(x)$ - $− \log(−f_i(x))$ is the potential of force field $F_i(x) = \frac{1}{f_i(x)}∇f_i(x)$ The forces balance at $x^\star(t)$: $$ F_0\left(x^{\star}(t)\right)+\sum_{i=1}^m F_i\left(x^{\star}(t)\right)=0 $$ **Algorithm** <i><u>Barrier Method</u></i> Given strictly feasible $x$, $t := t^{(0)} > 0$, $μ > 1$, tolerance $ε > 0$. Repeat: 1. Centering step: Compute $x^\star(t)$ by minimizing $tf_0 + \phi$, subject to $Ax = b$. 2. Update: $x:=x^\star(t)$. 3. Stopping criterion: quit if $\frac{m}{t} \leq \varepsilon$. (It follows from [proposition](Interior%20Point%20Methods%20in%20Inequality%20Constrained%20Minimization.md#^dgap)) 4. Increase: $t := μt$. **Fact** - Centering usually done using Newton’s method, starting at current $x$. - Choice of $μ$ involves a trade-off: large $μ$ means fewer outer iterations, more inner (Newton) iterations; typical values of $μ$ is in $[10, 20]$. ## Generalized Inequality Problems **Def** <i><u>Generalized Inequality Problem</u></i> The generalized inequality problem is defined as $$ \begin{aligned}\text{minimize } \quad & f_0(x)\\\text{subject to }\quad & f_i(x)\leq_{K_i} 0 \quad i=1,\dots,m \\& Ax=b \end{aligned} $$ with $f_0$ convex, $f_i:\R^{n} \to \R^{k_i}$ convex with respect to [proper cones]() $K_i \subseteq \R^{k_i}$ and twice continuously differentiable. $A∈\R^{p×n}$with $\mathop{\text{rank}}A=p$. **Def** <i><u>Generalized Logarithm</u></i> A function $\psi : \R^q → \R$ is a generalized logarithm for proper cone $K \subseteq \R^q$ if - $\text{dom}\psi = \text{int} K$ and $\nabla^2 \psi(y) \prec0$ for $y\succ_K 0$ - $ψ(sy)=ψ(y)+θ\log s$ for $y\succ_K0$, $s>0$(here $θ$ is called the degree of $ψ$) <b><u>e.g.</u></b> - Nonnegative orthant $K = \R^q_+$and $ψ(y) =\sum_{i=1}^n \log y_i$, with degree $θ = n$. - Positive semidefinite cone $K = \mathbb{S}_n^+$ and $\psi(Y)=\log \det Y$, with degree $\theta=n$. **Prop** For any $y\succ_K 0$, we have $$ \nabla \psi(y) \succeq_{K^{\star}} 0, \quad y^{\mathrm{T}}\nabla\psi(y)=\theta $$ **Def** <i><u>Generalized Logarithmic Barrier</u></i> For $f_1(x) \preceq_{K_1} 0, \dots , f_m(x) \preceq_{K_m} 0$, we define the generalized logarithmic barrier as: $$ \phi_{\text{general}}(x)=-\sum_{i=1}^m \psi_i(-f_i(x))\\ \text{dom}\phi=\{x \mid f_i(x)\prec_{K_i}0 \text{ for all } i\} $$ where $\psi_i$ is a generalized logarithmic function for $K_i$ with degree $\theta_i$. **Def** _Generalized Central Path_ The generalized central path is $\{ x^\star(t) \mid t>0 \}$ where $x^\star(t)$ solves $$ \begin{aligned}\text{minimize } \quad & tf_0(x)+\phi_{\text{general}}(x) \\\text{subject to } \quad & Ax=b\end{aligned} $$ **Thrm** $x = x^\star(t)$ if there exists a $w ∈\R^p$ such that $$ t \nabla f_0(x)+\sum_{i=1}^m (\mathrm{D} f_i(x))^{\mathrm{T}} \nabla \psi_i\left(-f_i(x)\right)+A^T w=0 $$ where $\mathrm{D}f_i(x) \in \R^{k_i \times n}$ is the derivative matrix of $f_i$. Therefore, $x^\star(t)$ minimizes Lagrangian $L(x, λ^\star(t), ν^\star(t))$ by rewriting the equation above, where $$ \lambda_i^{\star}(t)=\frac{1}{t} \nabla \psi_i\left(-f_i\left(x^{\star}\right)\right), \quad \nu^{\star}(t)=\frac{w}{t} $$ with duality gap$$ f_0\left(x^{\star}(t)\right)-g\left(\lambda^{\star}(t), \nu^{\star}(t)\right)=\frac{1}{t} \sum_{i=1}^m \theta_i $$^gdgap **Algorithm** <i><u>Generalized Barrier Method</u></i> Given strictly feasible $x$, $t := t^{(0)} > 0$, $\mu>1$, tolerance $ε > 0$. Repeat: 1. Centering step: Compute $x^\star(t)$ by minimizing $tf_0 + \phi$, subject to $Ax = b$. 2. Update: $x:=x^\star(t)$. 3. Stopping criterion: quit if $\frac{\sum_i\theta_i}{t} \leq \varepsilon$. (It follows from [proposition](Interior%20Point%20Methods%20in%20Inequality%20Constrained%20Minimization.md#^gdgap)) 4. Increase: $t := μt$.