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Chapter 3 Geometry of convex functions - Meboo Publishing ...

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220 CHAPTER 3. GEOMETRY OF CONVEX FUNCTIONS<br />

3.3.0.0.1 Example. Projecting the origin on an affine subset, in 1-norm.<br />

In (1844) we interpret least norm solution to linear system Ax = b as<br />

orthogonal projection <strong>of</strong> the origin 0 on affine subset A = {x∈ R n |Ax=b}<br />

where A∈ R m×n is fat full-rank. Suppose, instead <strong>of</strong> the Euclidean metric,<br />

we use taxicab distance to do projection. Then the least 1-norm problem is<br />

stated, for b ∈ R(A)<br />

minimize ‖x‖ 1<br />

x<br />

(496)<br />

subject to Ax = b<br />

Optimal solution can be interpreted as an oblique projection on A simply<br />

because the Euclidean metric is not employed. This problem statement<br />

sometimes returns optimal x ⋆ having minimum cardinality; which can be<br />

explained intuitively with reference to Figure 68: [19]<br />

Projection <strong>of</strong> the origin, in 1-norm, on affine subset A is equivalent to<br />

maximization (in this case) <strong>of</strong> the 1-norm ball until it kisses A ; rather, a<br />

kissing point in A achieves the distance in 1-norm from the origin to A . For<br />

the example illustrated (m=1, n=3), it appears that a vertex <strong>of</strong> the ball will<br />

be first to touch A . 1-norm ball vertices in R 3 represent nontrivial points <strong>of</strong><br />

minimum cardinality 1, whereas edges represent cardinality 2, while relative<br />

interiors <strong>of</strong> facets represent maximum cardinality 3. By reorienting affine<br />

subset A so it were parallel to an edge or facet, it becomes evident as we<br />

expand or contract the ball that a kissing point is not necessarily unique. 3.8<br />

The 1-norm ball in R n has 2 n facets and 2n vertices. 3.9 For n > 0<br />

B 1 = {x∈ R n | ‖x‖ 1 ≤ 1} = conv{±e i ∈ R n , i=1... n} (497)<br />

is a vertex-description <strong>of</strong> the unit 1-norm ball. Maximization <strong>of</strong> the 1-norm<br />

ball until it kisses A is equivalent to minimization <strong>of</strong> the 1-norm ball until it<br />

no longer intersects A . Then projection <strong>of</strong> the origin on affine subset A is<br />

where<br />

minimize<br />

x∈R n ‖x‖ 1<br />

subject to Ax = b<br />

≡<br />

minimize c<br />

c∈R , x∈R n<br />

subject to x ∈ cB 1<br />

Ax = b<br />

(498)<br />

cB 1 = {[I −I ]a | a T 1=c, a≽0} (499)<br />

3.8 This is unlike the case for the Euclidean ball (1844) where minimum-distance<br />

projection on a <strong>convex</strong> set is unique (E.9); all kissable faces <strong>of</strong> the Euclidean ball are<br />

single points (vertices).<br />

3.9 The ∞-norm ball in R n has 2n facets and 2 n vertices.

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