formulasearchengine - User contributions [en]

Relative permeability

2014-07-23T23:03:16Z

80.202.201.13: /* LET-type */

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River delta

2014-03-02T19:05:34Z

80.202.213.4:

It is very common to have a dental emergency -- a fractured tooth, an abscess, or severe pain when chewing. Over-the-counter pain medication is just masking the problem. Seeing an emergency dentist is critical to getting the source of the problem diagnosed and corrected as soon as possible. Here are some common dental emergencies: Toothache: The most common dental emergency. This generally means a badly decayed tooth. As the pain affects the tooth's nerve, treatment involves gently removing any debris lodged in the cavity being careful not to poke deep as this will cause severe pain if the nerve is touched. Next rinse vigorously with warm water. Then soak a small piece of cotton in oil of cloves and insert it in the cavity. This will give temporary relief until a dentist can be reached. At times the pain may have a more obscure location such as decay under an old filling. As this can be only corrected by a dentist there are two things you can do to help the pain. Administer a pain pill (aspirin or some other analgesic) internally or dissolve a tablet in a half glass (4 oz) of warm water holding it in the mouth for several minutes before spitting it out. DO NOT PLACE A WHOLE TABLET OR ANY PART OF IT IN THE TOOTH OR AGAINST THE SOFT GUM TISSUE AS IT WILL RESULT IN A NASTY BURN. Swollen Jaw: This may be caused by several conditions the most probable being an abscessed tooth. In any case the treatment should be to reduce pain and swelling. An ice pack held on the outside of the jaw, (ten minutes on and ten minutes off) will take care of both. If this does not control the pain, an analgesic tablet can be given every four hours. Other Oral Injuries: Broken teeth, cut lips, bitten tongue or lips if severe means a trip to a dentist as soon as possible. In the mean time rinse the mouth with warm water and place cold compression the face opposite the injury. If there is a lot of bleeding, apply direct pressure to the bleeding area. If bleeding does not stop get patient to the emergency room of a hospital as stitches may be necessary. Prolonged Bleeding Following Extraction: Place a gauze pad or better still a moistened tea bag over the socket and have the patient bite down gently on it for 30 to 45 minutes. The tannic acid in the tea seeps into the tissues and often helps stop the bleeding. If bleeding continues after two hours, call the dentist or take patient to the emergency room of the nearest hospital. Broken Jaw: If you suspect the patient's jaw is broken, bring the upper and lower teeth together. Put a necktie, handkerchief or towel under the chin, tying it over the head to immobilize the jaw until you can get the patient to a dentist or the emergency room of a hospital. Painful Erupting Tooth: In young children teething pain can come from a loose baby tooth or from an erupting permanent tooth. Some relief can be given by crushing a little ice and wrapping it in gauze or a clean piece of cloth and putting it directly on the tooth or gum tissue where it hurts. The numbing effect of the cold, along with an appropriate dose of aspirin, usually provides temporary relief. In young adults, an erupting 3rd molar (Wisdom tooth), especially if it is impacted, can cause the jaw to swell and be quite painful. Often the gum around the tooth will show signs of infection. Temporary relief can be had by giving aspirin or some other painkiller and by dissolving an aspirin in half a glass of warm water and holding this solution in the mouth over the sore gum. AGAIN DO NOT PLACE A TABLET DIRECTLY OVER THE GUM OR CHEEK OR USE THE ASPIRIN SOLUTION ANY STRONGER THAN RECOMMENDED TO PREVENT BURNING THE TISSUE. The swelling of the jaw can be reduced by using an ice pack on the outside of the face at intervals of ten minutes on and ten minutes off. If you have any kind of inquiries concerning where and how you can make use of [http://www.youtube.com/watch?v=90z1mmiwNS8 dentist DC], you could contact us at our webpage.

Hypot

2013-10-31T01:56:43Z

80.202.214.27: Added javascript to list of programming languages

[[Image:Osipkov-Merritt-Models.jpg|thumb|200px|Osipkov-Merritt distribution functions, derived from galaxy models obeying [[Jaffe's law]] in the density. The isotropic model, <math>f=f(E)</math>, is plotted with the heavy line.]]

'''Osipkov–Merritt models''' (named for Leonid Osipkov and [[David Merritt]]) are mathematical representations of spherical stellar systems ([[galaxies]], [[star clusters]], [[globular clusters]] etc.). The Osipkov-Merritt formula generates a one-parameter family of [[phase-space]] [[distribution function]]s that reproduce a specified density profile (representing stars) in a specified gravitational potential (in which the stars move). The density and potential need not be self-consistently related.
A free parameter adjusts the degree of velocity anisotropy, from [[isotropic]] to completely [[wikt:radial|radial]] motions. The method is a generalization of [[Eddington's formula]]<ref>[[Arthur Eddington|Eddington, A.]] (1916), [http://adsabs.harvard.edu/abs/1916MNRAS..76..572E The distribution of stars in globular clusters,] ''Mon. Not. R. Astron. Soc.'', '''76''', 572</ref> for constructing isotropic spherical models.

The method was derived independently by its two eponymous discoverers.<ref>Osipkov, L. P. (1979), [http://adsabs.harvard.edu/abs/1979PAZh....5...77O Spherical systems of gravitating bodies with an ellipsoidal velocity distribution,] ''Pis'ma v Astron. Zhur.'', '''5''', 77</ref><ref name="Merritt">[[David Merritt|Merritt, D.]] (1985), [http://adsabs.harvard.edu/abs/1985AJ.....90.1027M Spherical stellar systems with spheroidal velocity distributions,] ''Astron. J.'', '''90''', 1027</ref> The latter derivation includes two additional families of models (Type IIa, b) with tangentially anisotropic motions.

==Derivation==

According to [[Jeans's theorem]], the [[phase-space]] density of stars ''f'' must be expressible in terms of the isolating [[integrals of motion]], which in a spherical stellar system are the [[energy]] ''E'' and the [[angular momentum]] ''J''. The Osipkov-Merritt ''[[ansatz]]'' is

:<math>f = f(Q) = f(E+J^2/2r_a^2)</math>

where ''ra'', the "anisotropy radius", is a free parameter. This ''ansatz'' implies that ''f'' is constant on spheroids in velocity space since
:<math>
2Q = v_r^2 + (1+r^2/r_a^2)v_t^2 + 2\Phi(r)
</math>
where ''v''r, ''v''t are velocity components parallel and perpendicular to the radius vector ''r'' and Φ(''r'') is the [[gravitational potential]].

The density ''ρ'' is the integral over velocities of ''f'':
:<math>
\rho(r) = 2\pi\int\int f(E,J) v_t dv_t dv_r
</math>
which can be written
:<math>
\rho(r) = {2\pi\over r^2} \int_\Phi^0 dQ f(Q) \int_0^{2r^2(Q-\Phi)/(1+r^2/r_a^2)} dJ^2\left[2(Q-\Phi)-(J^2/r^2)(1+r^2/r_a^2)\right]^{-1/2}
</math>

or

:<math>
\rho(r) = {4\pi\over 1+r^2/r_a^2} \int_\Phi^0 dQ \sqrt{2(Q-\Phi)}f(Q).
</math>

This equation has the form of an [[Abel integral equation]] and can be inverted to give ''f'' in terms of ''ρ'':

:<math>

f(Q) = {\sqrt{2}\over 4\pi^2} {d\over dQ} \int_Q^0 {d\Phi\over\sqrt{\Phi-Q}} {d\rho^'\over d\Phi},\ \ \ \ \ \rho^'(\Phi) = \left[1+r(\Phi)^2/r_a^2\right]\rho\left[r(\Phi)\right].
</math>

==Properties==

Following a derivation similar to the one above, the velocity dispersions in an Osipkov–Merritt model satisfy

:<math>
{\sigma_r^2\over\sigma_t^2} = 1 + {r^2\over r_a^2}.
</math>

The motions are nearly radial (<math>\sigma_r\gg\sigma_t</math>) for <math>r\gg r_a</math> and nearly isotropic (<math>\sigma_r\approx\sigma_t</math>) for <math>r\ll r_a</math>. This is a desirable feature, since stellar systems that form via [[gravitational collapse]] have isotropic cores and radially-anisotropic envelopes.<ref>van Albada, T. (1983), [http://adsabs.harvard.edu/abs/1982MNRAS.201..939V Dissipationless galaxy formation and the R to the 1/4-power law,] ''Mon. Not. R. Astron. Soc.'', '''201''', 939</ref>

If ''ra'' is assigned too small a value, ''f'' may be negative for some ''Q''. This is a consequence of the fact that spherical mass models can not always be reproduced by purely radial orbits. Since the number of stars on an orbit can not be negative, values of ''ra'' that generate negative ''f'''s are unphysical. This result can be used to constrain the maximum degree of anisotropy of spherical galaxy models.<ref name=Merritt/>

In his 1985 paper, Merritt defined two additional families of models ("Type II") that have isotropic cores and tangentially anisotropic envelopes. Both families assume

:<math>f = f(E-J^2/2r_a^2)</math>.

In Type IIa models, the orbits become completely circular at ''r=ra'' and remain so at all larger radii.
In Type IIb models, stars beyond ''ra'' move on orbits of various eccentricities, although the motion is always biased toward circular. In both families, the tangential velocity dispersion undergoes a jump as ''r'' increases past ''ra''.

Carollo ''et al.'' (1995)<ref>Carollo, C. M. ''et al.'' (1995), [http://adsabs.harvard.edu/abs/1995MNRAS.276.1131C Velocity profiles of Osipkov-Merritt models,] ''Mon. Not. R. Astron. Soc.'', '''276''', 1131</ref> derive many observable properties of Type I Osipkov–Merritt models.

==Applications==

Typical applications of Osipkov–Merritt models include:

-- modelling of [[star cluster]]s,<ref>Lupton, R.'' et al.'' (1989), [http://adsabs.harvard.edu/abs/1989ApJ...347..201L The internal velocity dispersions of three young star clusters in the Large Magellanic Cloud,] ''Astrophys. J.'', '''347''', 201</ref> [[galaxies]],<ref>Nolthenius, R. and [[Holland Ford|Ford, H.]] (1987), [http://adsabs.harvard.edu/abs/1986ApJ...305..600N The mass and halo dispersion profile of M32,] ''Astrophys. J.'', '''305''', 600</ref> [[dark matter halo]]s <ref>Sotnikova, N. Ya. and Rodionov, S. A. (2008), [http://adsabs.harvard.edu/abs/2008arXiv0809.3946S Anisotropic Models of Dark Halos,] ''Astron. Lett.'', '''34''', 664-674</ref> and [[galaxy cluster]]s;<ref>Lokas, E. and Mamon, G. (2001), [http://adsabs.harvard.edu/abs/2001MNRAS.321..155L Properties of spherical galaxies and clusters with an NFW density profile,] ''Mon. Not. R. Astron. Soc.'', '''321''', 155</ref>

-- constructing anisotropic galaxy models for studies of dynamical [[Instability#Instabilities of stellar systems|instabilities]].<ref>May, A. and [[James Binney|Binney, J.]] (1986), [http://adsabs.harvard.edu/abs/1986MNRAS.221P..13M Testing the stability of stellar systems,] ''Mon. Not. R. Astron. Soc.'', '''221''', 13</ref><ref>Saha, P. (1991), [http://adsabs.harvard.edu/abs/1991MNRAS.248..494S Unstable modes of a spherical stellar system,] ''Mon. Not. R. Astron. Soc.'', '''248''', 494</ref>

==See also==

* [[Stellar dynamics]]

== References ==

{{reflist|2}}

{{DEFAULTSORT:Osipkov-Merritt model}}
[[Category:Astrophysics]]

Relative permeability

2013-08-31T20:50:45Z

80.202.39.62: /* Corey-type */

{{technical|date=June 2012}}
'''Stochastic approximation''' methods are a family of iterative [[stochastic optimization]] [[algorithm]]s that attempt to find zeroes or extrema of functions which cannot be computed directly, but only estimated via noisy observations.

Mathematically, this refers to solving:

:<math> \min_{x \in \Theta}\; f(x) = \mathbb E[F(x,\xi)] </math>
where the objective is to find the parameter <math>x \in \Theta</math>, which minimizes <math>f(x)</math> for some unknown random variable, <math>\xi </math>. Denoting <math>d</math> as the dimension of the parameter <math>x </math>, we can assume that while the domain <math>\Theta \subset \mathbb R^d </math> is known, the objective function, <math>f(x)</math>, cannot be computed exactly, but instead approximated via simulation. This can be intuitively explained as follows. <math>f(x)</math> is the original function we want to minimize. However, due to noise, <math>f(x)</math> can not be evaluated exactly. This situation is modeled by the function <math>F(x,\xi)</math>, where <math>\xi</math> represents the noise and is a random variable. Since <math>\xi</math> is a random variable, so is the value of <math>F(x,\xi)</math>. The objective is then to minimize <math>f(x)</math>, but through evaluating <math>F(x,\xi)</math>. A reasonable way to do this is to minimize the expectancy of <math>F(x,\xi)</math>, i.e., <math>\mathbb E[F(x,\xi)]</math>.

The first, and prototypical, algorithms of this kind are the '''Robbins-Monro''' and '''Kiefer-Wolfowitz''' algorithms.

==Robbins–Monro algorithm==
The Robbins–Monro algorithm, introduced in 1951 by [[Herbert Robbins]] and Sutton Monro,<ref name="rm">{{cite doi|10.1214/aoms/1177729586}}</ref> presented a methodology for solving a root finding problem, where the function is represented as an expected value. Assume that we have a function <math>M(x)</math>, and a constant <math>\alpha</math>, such that the equation <math>M(x) = \alpha</math> has a unique root at <math>x=\theta</math>. It is assumed that while we cannot directly observe the function <math>M(x)</math>, we can instead obtain measurements of the random variable <math>N(x)</math> where <math>\mathbb E[N(x)] = M(x)</math>. The structure of the algorithm is to then generate iterates of the form:

::<math>x_{n+1}-x_n=a_n(\alpha-N(x_n))</math>

Here, <math>a_1, a_2, \dots</math> is a sequence of positive step sizes. [[Herbert Robbins|Robbins]] and Monro proved <ref name="rm" />, Theorem 2 that <math>x_n</math> [[convergence of random variables|converges]] in <math>L^2</math> (and hence also in probability) to <math>\theta</math> provided that:
* <math>N(x)</math> is uniformly bounded,
* <math>M(x)</math> is nondecreasing,
* <math>M'(\theta)</math> exists and is positive, and
* The sequence <math>a_n</math> satisfies the following requirements:
:: <math>\qquad \sum^{\infty}_{n=0}a_n = \infty \quad \mbox{ and } \quad \sum^{\infty}_{n=0}a^2_n < \infty \quad </math>

A particular sequence of steps which satisfy these conditions, and was suggested by Robbins–Monro, have the form: <math>a_n=a/n</math>, for <math> a > 0 </math>. Other series are possible but in order to average out the noise in <math>N(x)</math>, the above condition must be met.

===Complexity results===
#If <math>f(x)</math> is twice continuously differentiable, and strongly convex, and the minimizer of <math>f(x)</math> belongs to the interior of <math>\Theta</math>, then the Robbins-Monro algorithm will achieve the asymptotically optimal convergence rate, with respect to the objective function, being <math>\mathbb E[f(x_n) - f^*] = O(1/n)</math>, where <math>f^*</math> is the minimal value of <math>f(x)</math> over <math>x \in \Theta</math>.<ref name="jsacks">{{cite doi|10.1214/aoms/1177706619}}</ref><ref name="NJLS">{{cite doi|10.1137/070704277}}</ref>
# Conversely, in the general convex case, where we lack both the assumption of smoothness and strong convexity, Nemirovski and Yudin <ref name="NYcomp">Problem Complexity and Method Efficiency in Optimization, A. Nemirovski and D. Yudin, ''Wiley -Intersci. Ser. Discrete Math'' '''15''' ''John Wiley'' ''New York'' (1983) .</ref> have shown that the asymptotically optimal convergence rate, with respect to the objective function values, is <math>O(1/\sqrt{n})</math>. They have also proven that this rate cannot be improved.

===Subsequent developments===
While the Robbins-Monro algorithm is theoretically able to achieve <math> O(1/n)</math> under the assumption of twice continuous differentiability and strong convexity, it can perform quite poorly upon implementation. This is primarily due to the fact that the algorithm is very sensitive to the choice of the step size sequence, and the supposed asymptotically optimal step size policy can be quite harmful in the beginning.<ref name="NJLS" /><ref name="jcsbook">Introduction to Stochastic Search and Optimization: Estimation, Simulation and Control, J.C. Spall, ''John Wiley'' ''Hoboken, NJ'', (2003).</ref>

To overcome this shortfall, Polyak and Juditsky,<ref name="pj">{{cite doi|10.1137/0330046}}</ref> presented a method of accelerating Robbins-Monro through the use of longer steps, and averaging of the iterates. The algorithm would have the following structure:
::<math> x_{n+1} - x_n = b_n(\alpha - N(x_n)), \qquad \bar{x}_n = \frac{1}{n} \sum^{n-1}_{i=0} x_i </math>
The convergence of <math> \bar{x}_n </math> to the unique root <math>\theta</math> relies on the condition that the step sequence <math>\{b_n\}</math> decreases sufficiently slowly. That is
::<math> b_n \rightarrow 0, \qquad \frac{b_n - b_{n+1}}{b_n} = o(b_n)</math>
Therefore, the sequence <math>b_n = n^{-\alpha}</math> with <math>0 < \alpha < 1</math> satisfies this restriction, but <math>\alpha = 1</math> does not, hence the longer steps. Under the assumptions outlined in the Robbins-Monro algorithm, the resulting modification will result in the same asymptotically optimal convergence rate <math>O(1/n)</math> yet with a more robust step size policy.<ref name ="pj" />

Prior to this, the idea of using longer steps and averaging the iterates had already been proposed by Nemirovski and Yudin <ref name="NY">On Cezari's convergence of the steepest descent method for approximating saddle points of convex-concave functions, A. Nemirovski and D. Yudin, ''Dokl. Akad. Nauk SSR'' '''2939''', (1978 (Russian)), Soviet Math. Dokl. '''19''' (1978 (English)).</ref> for the cases of solving the stochastic optimization problem with continuous convex objectives and for convex-concave saddle point problems. These algorithms were observed to attain the nonasymptotic rate <math>O(1/\sqrt{n})</math>.

==Kiefer-Wolfowitz algorithm==
The Kiefer-Wolfowitz algorithm,<ref name = "KW">{{cite doi|10.1214/aoms/1177729392}}</ref> was introduced in 1952, and was motivated by the publication of the Robbins-Monro algorithm. However, the algorithm was presented as a method which would stochastically estimate the maximum of a function. Let <math>M(x) </math> be a function which has a maximum at the point <math>\theta </math>. It is assumed that <math>M(x)</math> is unknown, however, certain observations <math>N(x)</math>, where <math>\mathbb E[N(x)] = M(x)</math>, can be made at any point <math>x</math>. The structure of the algorithm follows a gradient-like method, with the iterates being generated as follows:
::<math> x_{n+1} = x_n + a_n \bigg(\frac{N(x_n + c_n) - N(x_n -c_n)}{c_n} \bigg) </math>
where the gradient of <math>M(x)</math> is approximated using finite differences. The sequence <math>\{c_n\}</math> specifies the sequence of finite difference widths used for the gradient approximation, while the sequence <math>\{a_n\}</math> specifies a sequence of positive step sizes taken along that direction. Kiefer and Wolfowitz proved that, if <math>M(x)</math> satisfied certain regularity conditions, then <math>x_n</math> will converge to <math>\theta</math> provided that:
* The function <math>f(x)</math> has a unique point of maximum (minimum) and is strong concave (convex)
** The algorithm was first presented with the requirement that the function <math>f(\cdot)</math> maintains strong global convexity (concavity) over the entire feasible space. Given this condition is too restrictive to impose over the entire domain, Kiefer and Wolfowitz proposed that it is sufficient to impose the condition over a compact set <math>C_0 \subset \mathbb R^d</math> which is known to include the optimal solution.
*The selected sequences <math>\{a_n\}</math> and <math>\{c_n\}</math> must be infinite sequences of positive numbers such that:
::<math>\mbox{1. } \quad c_n \rightarrow 0, \quad a_n \rightarrow 0 \quad \mbox{ as } \quad n \rightarrow \infty </math>

::<math> \mbox{2. } \quad \sum^\infty_{n=0} a_n = \infty, \qquad \sum^\infty_{n=0} \frac{a^2_n}{c^2_n} < \infty </math>

A suitable choice of sequences, as recommended by Kiefer and Wolfowitz, would be <math>a_n = 1/n</math> and <math>c_n = n^{-1/3}</math>.

===Subsequent developments and important issues===
#The Kiefer Wolfowitz algorithm requires that for each gradient computation, at least <math>d+1</math> different parameter values must be simulated for every iteration of the algorithm, where <math>d </math> is the dimension of the search space. This means that when <math>d</math> is large, the Kiefer-Wolfowitz algorithm will require substantial computational effort per iteration, leading to slow convergence.
## To address this problem, Spall, proposed the use of [[Simultaneous perturbation stochastic approximation|simultaneous perturbations]] to estimate the gradient. This method would require only two simulations per iteration, regardless of the dimension <math>d</math>.<ref name = "Jsp">{{cite doi|10.1109/TAC.2000.880982}}</ref>
#In the conditions required for convergence, the ability to specify a predetermined compact set that fulfills strong convexity (or concavity) and contains the unique solution can be difficult to find. With respect to real world applications, if the domain is quite large, these assumptions can be fairly restrictive and highly unrealistic.

==Further developments==
An extensive theoretical literature has grown up around these algorithms, concerning conditions for convergence, rates of convergence, multivariate and other generalizations, proper choice of step size, possible noise models, and so on.<ref name="kushneryin">{{cite doi|10.1007/978-1-4899-2696-8}}</ref><ref>''Stochastic Approximation and Recursive Estimation'', Mikhail Borisovich Nevel'son and Rafail Zalmanovich Has'minskiĭ, translated by Israel Program for Scientific Translations and B. Silver, Providence, RI: American Mathematical Society, 1973, 1976. ISBN 0-8218-1597-0.</ref> These methods are also applied in [[control theory]], in which case the unknown function which we wish to optimize or find the zero of may vary in time. In this case, the step size <math>a_n</math> should not converge to zero but should be chosen so as to track the function.<ref name="kushneryin"/>, 2nd ed., chapter 3

[[C. Johan Masreliez]] and [[R. Douglas Martin]] were the first to apply
stochastic approximation to [[Robust statistics|robust]] [[estimation]].<ref>{{cite doi|10.1109/TIT.1975.1055386}}</ref>

==See also==
*[[Stochastic gradient descent]]
*[[Stochastic optimization]]
*[[Simultaneous perturbation stochastic approximation]]

==References==
{{reflist}}

{{DEFAULTSORT:Stochastic Approximation}}
[[Category:Stochastic optimization]]
[[Category:Statistical approximations]]

Test (assessment)

2012-08-31T06:45:32Z

80.202.75.217: /* History */

Four or five years ago, a reader of some of my columns bought the domain name jamesaltucher.com and gave it to me as a birthday gift. It was a total surprise to me. I didn't even know the reader. I hope one day we meet. Two years ago a friend of mine, Tim Sykes, insisted I had to have a blog. He set it up for me. He even wrote the "About Me". I didn't want a blog. I had nothing to say. But about 6 or 7 months ago I decided I wanted to take this blog seriously. I kept putting off changing the "About Me" which was no longer really about me and maybe never was. A few weeks ago I did a chapter in one of the books in Seth Godin's "The Domino Project". The book is out and called "No Idling". Mohit Pewar organized it (here's Mohit's blog) and sent me a bunch of questions recently. It's intended to be an interview on his blog but I hope Mohit forgives me because I want to use it as my new "About Me" also. 1. You are a trader, investor, writer, and entrepreneur? Which of these roles you enjoy the most and why? When I first moved to New York City in 1994 I wanted to be everything to everyone. I had spent the six years prior to that writing a bunch of unpublished novels and unpublished short stories. I must've sent out 100s of stories to literary journals. I got form rejections from every publisher, journal, and agent I sent my novels and stories to. Now, in 1994, everything was possible. The money was in NYC. Media was here. I lived in my 10�10 room and pulled suits out of a garbage bag every morning but it didn't matter...the internet was revving up and I knew how to build a [http://Www.reddit.com/r/howto/search?q=website website]. One of the few in the city. My sister warned me though: nobody here is your friend. Everybody wants something 
And I wanted something. I wanted the fleeting feelings of success, for the first time ever, in order to feel better about myself. I wanted a girl next to me. I wanted to build and sell companies and finally prove to everyone I was the smartest. I wanted to do a TV show. I wanted to write books 
But everything involved having a master. Clients. Employers. Investors. Publishers. The market (the deadliest master of all). Employees. I was a slave to everyone for so many years. And the more shackles I had on, the lonelier I got 
Much of the time, even when I had those moments of success, I didn't know how to turn it into a better life. I felt ugly and then later, I felt stupid when I would let the success dribble away down the sink 
I love writing because every now and then that ugliness turns into honesty. When I write, I'm only a slave to myself. When I do all of those other things you ask about, I'm a slave to everyone else 
Some links 
33 Unusual Tips to Being a Better Write 
"The Tooth 
(one of my favorite posts on my blog 
2. What inspires you to get up and start working/writing every day 
The other day I had breakfast with a fascinating guy who had just sold a piece of his fund of funds. He told me what "fracking" was and how the US was going to be a major oil player again. We spoke for two hours about a wide range of topics, including what happens when we can finally implant a google chip in our brains 
After that I had to go onto NPR because I firmly believe that in one important respect we are degenerating as a country - we are graduating a generation of indentured servants who will spend 50 years or more paying down their student debt rather than starting companies and curing cancer. So maybe I made a difference 
Then I had lunch with a guy I hadn't seen in ten years. In those ten years he had gone to jail and now I was finally taking the time to forgive him for something he never did to me. I felt bad I hadn't helped him when he was at his low point. Then I came home and watched my kid play clarinet at her school. Then I read until I fell asleep. Today I did nothing but write. Both days inspired me 
It also inspires me that I'm being asked these questions. Whenever anyone asks me to do anything I'm infinitely grateful. Why me? I feel lucky. I like it when someone cares what I think. I'll write and do things as long as anyone cares. I honestly probably wouldn't write if nobody cared. I don't have enough humility for that, I'm ashamed to admit 
3. Your new book "How to be the luckiest person alive" has just come out. What is it about 
When I was a kid I thought I needed certain things: a college education from a great school, a great home, a lot of money, someone who would love me with ease. I wanted people to think I was smart. I wanted people to think I was even special. And as I grew older more and more goals got added to the list: a high chess rating, [http://www.pcs-systems.co.uk/Images/celinebag.aspx http://www.pcs-systems.co.uk/Images/celinebag.aspx] a published book, perfect weather, good friends, respect in various fields, etc. I lied to myself that I needed these things to be happy. The world was going to work hard to give me these things, I thought. But it turned out the world owed me no favors 
And gradually, over time, I lost everything I had ever gained. Several times. I've paced at night so many times wondering what the hell was I going to do next or trying not to care. The book is about regaining your sanity, regaining your happiness, finding luck in all the little pockets of life that people forget about. It's about turning away from the religion you've been hypnotized into believing into the religion you can find inside yourself every moment of the day 
[Note: in a few days I'm going to do a post on self-publishing and also how to get the ebook for free. The link above is to the paperback. Kindle should be ready soon also. 
Related link: Why I Write Books Even Though I've Lost Money On Every Book I've Ever Writte 
4. Is it possible to accelerate success? If yes, how 
Yes, and it's the only way I know actually to achieve success. It's by following the Daily Practice I outline in this post: 
It's the only way I know to exercise every muscle from the inside of you to the outside of you. I firmly believe that happiness starts with that practice 
5. You say that discipline, persistence and psychology are important if one has to achieve success. How can one work on improving "psychology" part 
Success doesn't really mean anything. People want to be happy in a harsh and unforgiving world. It's very difficult. We're so lucky most of us live in countries without major wars. Our kids aren't getting killed by random gunfire. We all have cell phones. We all can communicate with each other on the Internet. We have Google to catalog every piece of information in history! We are so amazingly lucky already 
How can it be I was so lucky to be born into such a body? In New York City of all places? Just by being born in such a way on this planet was an amazing success 
So what else is there? The fact is that most of us, including me, have a hard time being happy with such ready-made success. We quickly adapt and want so much more out of life. It's not wars or disease that kill us. It's the minor inconveniences that add up in life. It's the times we feel slighted or betrayed. Or even slightly betrayed. Or overcharged. Or we miss a train. Or it's raining today. Or the dishwasher doesn't work. Or the supermarket doesn't have the food we like. We forget how good the snow tasted when we were kids. Now we want gourmet food at every meal 
Taking a step back, doing the Daily Practice I outline in the question above. For me, the results of that bring me happiness. That's success. Today. And hopefully tomorrow 
6. You advocate not sending kids to college. What if kids grow up and then blame their parents about not letting them get a college education 
I went to one of my kid's music recitals yesterday. She was happy to see me. I hugged her afterwards. She played "the star wars theme" on the clarinet. I wish I could've played that for my parents. My other daughter has a dance recital in a few weeks. I tried to give her tips but she laughed at me. I was quite the breakdancer in my youth. The nerdiest breakdancer on the planet. I want to be present for them. To love them. To let them always know that in their own dark moments, they know I will listen to them. I love them. Even when they cry and don't always agree with me. Even when they laugh at me because sometimes I act like a clown 
Later, if they want to blame me for anything at all then I will still love them. That's my "what if" 
Two posts 
I want my daughters to be lesbian 
Advice I want to give my daughter 

7. Four of your favorite posts from The Altucher Confidential 
As soon as I publish a post I get scared to death. Is it good? Will people re-tweet? Will one part of the audience of this blog like it at the expense of another part of the audience. Will I get Facebook Likes? I have to stop clinging to these things but you also need to respect the audience. I don't know. It's a little bit confusing to me. I don't have the confidence of a real writer yet 
Here are four of my favorites 
How I screwed Yasser Arafat out of $2mm (and lost another $100mm in the process 
It's Your Fault 
I'm Guilty of Torturing Wome 
The Girl Whose Name Was a Curs 
Although these three are favorites I really don't post anything unless it's my favorite of that moment 
8. 3 must-read books for aspiring entrepreneurs 
The key in an entrepreneur book: you want to learn business. You want to learn how to honestly communicate with your customers. You want to stand out 
The Essays of Warren Buffett by Lawrence Cunningha 
"The Thank you Economy" by Gary Vaynerchu 
"Purple cow" by Seth Godi 
9. I love your writing, so do so many others out there. Who are your favorite writers 
"Jesus's Son" by Denis Johnson is the best collection of short stories ever written. I'm afraid I really don't like his novels though 
"Tangents" by M. Prado. A beautiful series of graphic stories about relationships 
Other writers: Miranda July, Ariel Leve, Mary Gaitskill, Charles Bukowski, Celine, Sam Lipsyte, William Vollmann, Raymond Carver. Arthur Nersesian. Stephen Dubner 
Many writers are only really good storytellers. Most writers come out of a cardboard factory MFA system and lack a real voice. A real voice is where every word exposes ten levels of hypocrisy in the world and brings us all the way back to see reality. The writers above have their own voices, their own pains, and their unique ways of expressing those pains. Some of them are funny. Some a little more dark. I wish I could write 1/10 as good as any of them 
10. You are a prolific writer. Do you have any hacks that help you write a lot in little time 
Coffee, plus everything else coffee does for you first thing in the morning 
Only write about things you either love or hate. But if you hate something, try to find a tiny gem buried in the bag of dirt so you can reach in when nobody is looking and put that gem in your pocket. Stealing a diamond in all the shit around us and then giving it away for free via writing is a nice little hack, Being fearless precisely when you are most scared is the best hack 
11. I totally get and love your idea about bleeding as a writer, appreciate if you share more with the readers of this blog 
Most people worry about what other people think of them. Most people worry about their health. Most people are at a crossroads and don't know how to take the next step and which road to take it on. Everyone is in a perpetual state of 'where do I put my foot next'. Nobody, including me, can avoid that 
You and I both need to wash our faces in the morning, brush our teeth, shower, shit, eat, fight the weather, fight the colds that want to attack us if we're not ready. Fight loneliness or learn how to love and appreciate the people who want to love you back. And learn how to forgive and love the people who are even more stupid and cruel than we are. We're afraid to tell each other these things because they are all both disgusting and true 
You and I both have the same color blood. If I cut my wrist open you can see the color of my blood. You look at it and see that it's the same color as yours. We have something in common. It doesn't have to be shameful. It's just red. Now we're friends. No matter whom you are or where you are from. I didn't have to lie to you to get you to be my friend 
Related Links 
How to be a Psychic in Ten Easy Lesson 
My New Year's Resolution in 199 
12. What is your advice for young entrepreneurs 
Only build something you really want to use yourself. There's got to be one thing you are completely desperate for and no matter where you look you can't find it. Nobody has invented it yet. So there you go - you invent it. If there's other people like you, you have a business. Else. You fail. Then do it again. Until it works. One day it will 
Follow these 100 Rules 
The 100 Rules for Being a Good Entrepreneur 
And, in particular this 
The Easiest Way to Succeed as an Entrepreneu 
In my just released book I have more chapters on my experiences as an entrepreneur 
13. I advocate the concept of working at a job while building your business. You have of course lived it. Now as you look back, what is your take on this? Is it possible to make it work while sailing on two boats 
Your boss wants everything out of you. He wants you to work 80 hours a week. He wants to look good taking credit for your work. He wants your infinite loyalty. So you need something back 
Exploit your employer. It's the best way to get good experience, clients, contacts. It's a legal way to steal. It's a fast way to be an entrepreneur because you see what large companies with infinite money are willing to pay for. If you can provide that, you make millions. It's how many great businesses have started and will always start. It's how every exit I've had started 
14. Who is a "person with true moral fiber"? In current times are there any role models who are people with true moral fiber 
I don't really know the answer. I think I know a few people like that. I hope I'm someone like that. And I pray to god the people I'm invested in are like that and my family is like that 
I find most people to be largely mean and stupid, a vile combination. It's not that I'm pessimistic or cynical. I'm very much an optimist. It's just reality. Open the newspaper or turn on the TV and watch these people 
Moral fiber atrophies more quickly than any muscle on the body. An exercise I do every morning is to promise myself that "I'm going to save a life today" and then leave it in the hands of the Universe to direct me how I can best do that. Through that little exercise plus the Daily Practice described above I hope to keep regenerating that fiber 
15. Your message to the readers of this blog 
Skip dinner. But follow me on Twitter. 
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