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Proof lnexy = xy = lnex lney = ln(ex ·ey) Since lnx is onetoone, then exy = ex ·ey 1 = e0 = ex(−x) = ex ·e−x ⇒ e−x = 1 ex ex−y = ex(−y) = ex ·e−y = ex · 1 ey ex ey • For r = m ∈ N, emx = e z }m { x···x = z }m { ex ···ex = (ex)m • For r = 1 n, n ∈ N and n 6= 0, ex = e n n x = e 1 nx n ⇒ e n x = (ex) 1 • For r rational, let r = m n, m, n ∈ N. The letters, or pronumerals, mean different things in varying contexts For example 'd' can refer to distance, or displacement in different circumstances. N X } X f ނ ̖ f ޏW B N X } X ̃C X g ǎ A C R p f ށE N X } X J h ȂǁB since T C g ́A 1024 ~768 T C Y ō쐬 AIE60 œ m F Ă ܂ B ̑ ̃u E U ł́A ɓ 삵 ܂ B.

Proof lnexy = xy = lnex lney = ln(ex ·ey) Since lnx is onetoone, then exy = ex ·ey 1 = e0 = ex(−x) = ex ·e−x ⇒ e−x = 1 ex ex−y = ex(−y) = ex ·e−y = ex · 1 ey ex ey • For r = m ∈ N, emx = e z }m { x···x = z }m { ex ···ex = (ex)m • For r = 1 n, n ∈ N and n 6= 0, ex = e n n x = e 1 nx n ⇒ e n x = (ex) 1 • For r rational, let r = m n, m, n ∈ N. 0 I E C I F 3 5 ;. 2 (a) Define uniform continuity on R for a function f R → R (b) Suppose that f,g R → R are uniformly continuous on R (i) Prove that f g is uniformly continuous on R (ii) Give an example to show that fg need not be uniformly continuous on R Solution • (a) A function f R → R is uniformly continuous if for every ϵ > 0 there exists δ > 0 such that f(x)−f(y) < ϵ for all x.

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2 (a) Define uniform continuity on R for a function f R → R (b) Suppose that f,g R → R are uniformly continuous on R (i) Prove that f g is uniformly continuous on R (ii) Give an example to show that fg need not be uniformly continuous on R Solution • (a) A function f R → R is uniformly continuous if for every ϵ > 0 there exists δ > 0 such that f(x)−f(y) < ϵ for all x. `V` dghVX^hr \^cr, `dchfda^fibiä eadhrä, ^ Xd_h^ X cdXiä \^cr, YZ XaVZqmghXih Xåhd_ @ik D Xdh dZcV\Zq, X 1997 YdZi, å dhmha^Xd igaqnVa. _ C X g C J i52cm ~31cm j _ C A X g i52cm ~31cm j H ꂩ ̒ ^ ̔ P ʂ́C V g1 o ܂ B.

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í S ^ 2(#Ø'¼ b0Û o 2 b v 8'¼ r S " 'g/ ;. ¼ í » ¶ w ß Q M p x g ¯ µ Ä q ¼ í Æ ù ¨ ¼ t ¯ µ Ä Ì å ï µ ^ d \ q t 7 & s g M O \ q t K { \ x \ æ ó w Ô × $ s g t ï ¿ X Æ ;. And since the P^(j) n all have the same distribution, taking expectations yields, E D(P^ n1kQ) E D(P^ nkQ) as claimed 5 Estimating the entropy The fact that EH(P^ n) H(P) follows from the concavity of the entropy (using Jensen’s inequality), upon noting that E(P^.

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ë " ` j ` \ Í ç · ¸$'2$$ ¶ ñ ¢ ç · Æ ê µ å ñ Ï ¢ ¢ ñ ½ È µ å È é & ¸ Ï ¢ ¢ ñ ½ È µ å È é J y ¾ ñ ¶ å ñ Ï ¢ ¢ ñ ½ È µ å È é & ¸ Ï ¢ ¢ ñ ½ È µ å È é. Zò×^= Ò °q­8m¡ ÒBo Á#§Rä´ï¶O j' x ÷ G¢ {ó@°mæ ÿÎvø¥ÒYqµå 3sT¥ø 4bì·Çùù XíÔæªÆÀ5' $ í á PPò Y©)nR Ç# ïÕ r ©ë)í ¿Y³ÁqØÐLè¤Öb c x!_w½£xKrU¦óí ví/XÌØ S Created Date zò×^= Ò °q­8m¡ Ê Í \Êhý 3³KgÐ Þ`bZ ë 6{Ú2 ¢ Ç. Convex Optimization — Boyd & Vandenberghe 3 Convex functions • basic properties and examples • operations that preserve convexity • the conjugate function.

Justifications that e i = cos() i sin() e i x = cos( x ) i sin( x ) Justification #1 from the derivative Consider the function on the right hand side (RHS) f(x) = cos( x ) i sin( x ) Differentiate this function. 3 7 Í v H  a ä c Ï H Á i I d V ú k ^ Í N Î ¥ < ™ w C F n Ë ¥ ä Â È x i c Ï = v æ e V f J s é o Á a ú k D Î „ ñ j I g H Æ l K d h Â Ë > U q j ö F ¥ l ) D Á i t Ï æ H ù, v é # Í V = A Æ k d C ¶ g N ä u h < o J s n f I ;.

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Ae Aeœ E µe A Ae C Aºœae Ae Aº E Vicjuan S A Aººa A Cs A C

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