标签：red   必须   linear   \n   rspec   inf   where   normal   sqrt   

目录

• CVX notes
  • Preliminaries
    • 1. PSD
    • 2. Matrix norm
    • 3. Duality
  • Convex sets
  • Convex functions
    • 1. Closed convex
    • 2. Level sets
    • 3. Operations perserving convexity of functions
    • 4. Detect convexity
    • 5. Subgradient
    • 6. Optimality conditions
    • 7. Strong convexity
  • Lagrange Duality
  • Cones
  • Algorithm convergence

CVX notes

Preliminaries

1. PSD

M is positive semidefinite matrix \(\iff\) all principal submatrices \(P\) of \(M\) are PSD

Note: This follows by considering the quadratic form \(x^T Mx\) and looking at the components of \(x\) corresponding to the defining subset of principal submatrix. The converse is trivially true.

M is PSD \(\iff\) all principal minors are non-negative (所有主子式非负)

将M写成二次型：
\[ x^T M x = \sum_{i,j}M_{ij}x_ix_j \]
于是取 \(x\) 为标准基 \(e_i ~\implies M_{ii} \ge 0 \implies \mathbf{tr}(M) \ge 0\) , 再取\(x\)为零向量只有 i，j两个位置为 1，则
\[ \begin{gathered} x^T M x = M_{ii}M_{jj} - M_{ij}^2 \ge 0 ~~(PSD) \\implies M_{ij} \le \sqrt{M_{ii}M_{jj}} \le \frac{M_{ii} + M_{jj}}{2} \end{gathered} \]

2. Matrix norm

General definition of a norm:

Matrix norm:

• Frobenius norm: \(\|A\|_F := \sqrt{\langle A,A\rangle_F} = \sqrt{\mathbf{tr}(A^*A)}\)
• Induced norm: \(\|A\|_p := \sup_\limits{\|x\|_p = 1} \|Ax\|_p\)
• Nuclear norm: \(\|A\|_{nuclear} := \sum \sigma_i(A)\) (奇异值之和)
• Spectral norm: \(\|A\|_{spectral} := \lambda_1\) (最大特征值)

Spectrial radius

3. Duality

Two ==equivalent== ways to represent a convex set:

• standard representation: The family of points in the set
• dual representation: The set of halfspaces containing the set (半平面的交集)

A closed convex set \(S\) is the intersection of all closed halfspaces \(H\) containing it.

Polar
Let \(S \subseteq \mathbb{R}^n\) be a convex set containing the origin. The polar of \(S\) is defined as follows
\[ S^{\circ} := \{y ~|~ y^Tx \le 1, ~\forall x \in S\} \]

Note

• polar is one way of representing the all halfspaces containing a convex set
• every halfspace \(a^Tx \le b\) with \(b \neq 0\) can be written as a “normalized” inequality \(y^T x \le 1\), by dividing by \(b\)
• \(S^{\circ}\) can be thought of as the normalized representations of halfspaces containing \(S\)

Properties of the polar:

1. \(S^{\circ\circ} = S\)
2. \(S^{\circ}\) is a closed convex set containing the origin
3. When 0 is in the interior of \(S\), then \(S^{\circ}\) is bounded
4. When \(S\) is non-convex, \(S^{\circ} = (\mathbf{conv}(S))^{\circ}\), and \(S^{\circ\circ} = \mathbf{conv}(S)\)

Polar duality of convex cones

Notes

1. \(K^{\circ\circ} = K\)
2. \(K^{\circ}\) is closed and convex

Conjugation of convex functions
Let \(f: \mathbb{R}^n \mapsto \mathbb{R}\cup\{\infty\}\) be a convex function. The ==conjugation== of \(f\) is
\[ f^*(y) := \sup_\limits{x}(y^Tx - f(x)) \]

Properties of the conjugate

1. \(f^{**} = f\)
2. \(f^*\) is convex (supremum of affine functions of \(y\))

Convex sets

Convex functions

1. affine is convex: \(f(x) = a^T x+b\)

   affine 既凸也凹

2. 任何_范数_是凸的

   Proof: let \(\pi(x)\) be a norm of \(x\), then

3. \(f\) is convex \(\iff\) epi(\(f\)) is convex

1. Closed convex

A convex function \(f\) is called closed if its epigraph is a closed set.

1. \(f\) which is convex and continuous on a closed domain is a closed function. (norms)
2. all differentiable convex functions are closed with dom\(f = \mathbb{R}^n\).
3. 当考虑一个凸函数时，通常认为在dom\(f\)外取值为\(\infty\)
4. Jensen‘s inequality:

5. Corollary:

   pf: \(f(x) = f(\sum\alpha_i x_i) \le \sum \alpha_i f(x_i) \le \max_\limits{i} f(x_i)\)

2. Level sets

Note: the convexity of level sets does not characterize convex functions, but quasiconvex functions.

1. convex \(f\) is closed \(\implies\) all its level sets are closed

Some convex sets

1. norm ball (\(\{x\in \mathbb{R}^n | \|x\| \le 1\}\)) is convex and closed

2. 椭球(\(\{x | (x-a)^T Q (x-a) \le r^2\}\)) is convex and closed

   pf: \(x^TQy := \langle x, y \rangle\) 满足内积的三条性质

   • bilinearity
   • symmetry
   • positivity
     上述三条性质 \(\iff\) Q is PSD

3. \(\epsilon\)-neighborhood:

3. Operations perserving convexity of functions

1. stability under taking weighted sums: \(f,g \mapsto \lambda f + \mu g, \; \lambda,\mu \ge 0\)
2. stability under affine substitutions of the argument: \(x \mapsto Ax+b\) or \(f(x) \mapsto \phi(x) = f(Ax+b)\)
3. stability under taking pointwise sup: \(\{f_i\}_{i \in \mathcal{I}} \mapsto g(x) := \sup_\limits{i \in \mathcal{I}}f_i(x)\), 凸函数族 \(\{f_i\}_{i \in \mathcal{I}}\) 逐点取上确界而成的函数也是凸的
4. stability under partial minimization: \(f(x,y)\) jointly convex in \((x,y)\), then \(g(x) = \inf_\limits{y} f(x,y)\) is convex (suppose g is proper, i.e., > -\(\infty\) everywhere and is finite at least at one point)
5. stability under perspective: \(f(x) \mapsto g(x,t) = tf(x/t), \mathbf{dom}g = \{(x,t) | x/t \in \mathbf{dom}f, t > 0\}\)

4. Detect convexity

Necessary and Sufficient Convexity Condition for smooth function:

• 一阶可微的光滑函数 \(f\) 是凸的 \(\iff\) \(f‘(x)\) 单调非减
• 二阶可微的光滑函数 \(f\) 是凸的 \(\iff\) \(f‘‘(x) \ge 0\)

subgradient property is characteristic of convex functions:

5. Subgradient

Examples

6. Optimality conditions

凸函数的局部最优等价于全局最优。

第一充要条件（凸函数）
\(x^* \in \mathbf{dom}f?\) is the minimizer \(\iff?\) \(0 \in \partial f(x^*)?\)

7. Strong convexity

A differentiable function f is strongly convex if
\[ f(y) \ge f(x) + \nabla f(x)^T(y-x) + \frac{\mu}{2} \|y-x\|^2 \]

Note

1. \(f\) is not necessarily differentiable, (see the equivalent definition)
2. if \(f\) is non-smooth, gradient -> subgradient
3. strong convexity \(\implies\) strict convexity

Note: Intuitively speaking, strong convexity means that there exists a quartic lower bound on the growth of the function.

Equivalent definition
\[ \begin{align} &(i)~f(y)\ge f(x)+\nabla f(x)^T(y-x)+\frac{\mu}{2}\lVert y-x \rVert^2,~\forall x, y.\ &(ii)~g(x) = f(x)-\frac{\mu}{2}\lVert x \rVert^2~\text{is convex},~\forall x.\ &(iii)~\langle \nabla f(x) - \nabla f(y),x-y \rangle \ge \mu \lVert x-y\rVert^2,~\forall x, y.\ &(iv)~f(\alpha x+ (1-\alpha) y) \le \alpha f(x) + (1-\alpha) f(y) - \frac{\alpha (1-\alpha)\mu}{2}\Vert x-y\rVert^2,~\alpha \in [0,1].\ &(v)~\nabla^2 f(x) \succeq \mu \boldsymbol{I} \end{align} \]

Lagrange Duality

Consider an optimization problem in standard form (not necessarily convex)
\[ \begin{array}{ll} \underset{x}{\text{minimize}} & f_0(x) \\text{subject to} & f_i(x) \le 0, ~i=1,\cdots,m \~ & h_i(x) = 0, ~i=1,\cdots,p \end{array} \]

The Lagrangian is
\[ L(x,\boldsymbol{\lambda},\boldsymbol{\mu}) = f_0(x) + \sum_{i=1}^m \lambda_i f_i(x) + \sum_{i=1}^p \mu_i h_i(x) \]

The Lagrange dual function is defined as
\[ g(\lambda, \mu) = \inf_{x} L(x,\lambda,\mu) \]

Lagrange dual problem
\[ \begin{array}{ll} \underset{\lambda, \mu}{\text{maximize}} & g(\lambda, \mu) \\text{subject to} & \boldsymbol{\lambda} \succeq \mathbf{0} \end{array} \]

Weak duality
\[ d^* \le p^* \]

• \(d^*\): optima of dual problem
• \(p^*\): optima of primal problem
• duality gap: \(p^* - d^*\)
• always hold

Strong dualiy
\[ d^* = p^* \]

• constraint qualifications \(\implies\) strong duality
• Slater’s Constraint Qualification: a convex problem is strictly feasible (i.e., \(\exists ~x \in \mathbf{int} \mathcal{D}: x \in \Omega\))

Complementary slackness

KKT conditions

Cones

Tagent cone
Let M be a (nonempty) convex set and \(x^* \in M\), the tagent cone of \(M\) at \(x^*\) is the cone
\[ \begin{split} T_M(x^*) &= \{h \in \mathbb{R}^n | x^* + th \in M, \; \forall t > 0 \} \&= \{y \in \mathbb{R}^n ~|~ y - x^* \in M\} \end{split} \]

Note:

• Geometrically, this is the set of all directions leading from \(x^*\) inside \(M\)
• convex but not necessarily closed
• fact: if \(x^*\) is a minimizer, then \(\forall h \in T_M(x^*) \implies h^T \nabla f(x^*) \ge 0\). （因为tangent cone里面都是可行解，所以必须不是下降方向）
• \(T_M(x^*) = \mathbb{R}^n \iff x^* \in \mathbf{int}M\)

e.g. 多面体
\[ M = \{x | Ax \le b\} = \{x | a_i^Tx \le b_i, \; i = 1,\dots,m\} \]
the tangent cone at \(x^*\) is
\[ T_M(x^*) = \{h~|~a_i^T h \le 0, ~\forall i, ~a_i^T x^* = b_i\} \]

Normal cone: the polar cone of tangent cone
\[ N_M(x^*) = \{g \in \mathbb{R}^n ~|~ \langle g, y-x^*\rangle \le 0, ~\forall y \in M\} \]

Note:

• normal cone is the polar to tangent cone, i.e.,
  \[ \begin{split} T_M(x^*) &= \{g \in \mathbb{R}^n ~|~ \langle g, y-x^*\rangle \ge 0, ~\forall y \in M\} \N_M(x^*) &= \{g \in \mathbb{R}^n ~|~ \langle g, y-x^*\rangle \le 0, ~\forall y \in M\} \end{split} \]
• fact: if \(x^*\) is a minimizer, then \(-\nabla f(x^*) \in N_M(x^*)\).
• 

Algorithm convergence

CVX notes

标签：red   必须   linear   \n   rspec   inf   where   normal   sqrt   

原文地址：https://www.cnblogs.com/yychi/p/9398439.html