Linear Programming Quotes

The Government set the stage economically by informing everyone that we were in a depression period, with very pointed allusions to the 1930s. The period just prior to our last 'good' war. ... Boiled down, our objective was to make killing and military life seem like adventurous fun, so for our inspiration we went back to the Thirties as well. It was pure serendipity. Inside one of the Scripter offices there was an old copy of Doc Smith's first LENSMAN space opera. It turned out that audiences in the 1970s were more receptive to the sort of things they scoffed at as juvenilia in the 1930s. Our drugs conditioned them to repeat viewings, simultaneously serving the ends of profit and positive reinforcement. The movie we came up with stroked all the correct psychological triggers. The fact that it grossed more money than any film in history at the time proved how on target our approach was.' 'Oh my God... said Jonathan, his mouth stalling the open position. 'Six months afterward we ripped ourselves off and got secondary reinforcement onto television. We pulled a 40 share. The year after that we phased in the video games, experimenting with non-narcotic hypnosis, using electrical pulses, body capacitance, and keying the pleasure centers of the brain with low voltage shocks. Jesus, Jonathan, can you *see* what we've accomplished? In something under half a decade we've programmed an entire generation of warm bodies to go to war for us and love it. They buy what we tell them to buy. Music, movies, whole lifestyles. And they hate who we tell them to. ... It's simple to make our audiences slaver for blood; that past hasn't changed since the days of the Colosseum. We've conditioned a whole population to live on the rim of Apocalypse and love it. They want to kill the enemy, tear his heart out, go to war so their gas bills will go down! They're all primed for just that sort of denouemment, ti satisfy their need for linear storytelling in the fictions that have become their lives! The system perpetuates itself. Our own guinea pigs pay us money to keep the mechanisms grinding away. If you don't believe that, just check out last year's big hit movies... then try to tell me the target demographic audience isn't waiting for marching orders. ("Incident On A Rainy Night In Beverly Hills")

Recent psychological research on grief favors meaning making over closure; accepts zigzagging paths, not just linear stages; recognizes ambiguity without pathology; and acknowledges continuing bonds between the living and the dead rather than commanding decathexis. But old ideas about grief as a linear march to closure still hold powerful sway. Many psychologists and grief counseling programs continue to consider “closure” a therapeutic goal. Sympathy cards, internet searches, and friendly advice often uphold a rigid division between healthy grief that the mourner “gets over” and unhealthy grief that persists. Forensic exhumation, too, continues to be informed by these deeply rooted ideas. The experiences of grief and exhumation related by families of the missing indicate something more complex and mysterious than “closure.” Exhumation heals and wounds, sometimes both at once, in the same gesture, in the same breath, as Dulce described feeling consoled and destroyed by the fragment of her brother’s bones. Exhumation can divide brothers and restore fathers, open old wounds and open the possibility of regeneration—of building something new with the “pile of broken mirrors” that is memory, loss, and mourning.

Still, I think that one of the most fundamental problems is want of discipline. Homes that severely restrict viewing hours, insist on family reading, encourage debate on good books, talk about the quality and the morality of television programs they do see, rarely or never allow children to watch television without an adult being present (in other words, refusing to let the TV become an unpaid nanny), and generally develop a host of other interests, are not likely to be greatly contaminated by the medium, while still enjoying its numerous benefits. But what will produce such families, if not godly parents and the power of the Holy Spirit in and through biblical preaching, teaching, example, and witness? The sad fact is that unless families have a tremendously strong moral base, they will not perceive the dangers in the popular culture; or, if they perceive them, they will not have the stamina to oppose them. There is little point in preachers disgorging all the sad statistics about how many hours of television the average American watches per week, or how many murders a child has witnessed on television by the age of six, or how a teenager has failed to think linearly because of the twenty thousand hours of flickering images he or she has watched, unless the preacher, by the grace of God, is establishing a radically different lifestyle, and serving as a vehicle of grace to enable the people in his congregation to pursue it with determination, joy, and a sense of adventurous, God-pleasing freedom. Meanwhile, the harsh reality is that most Americans, including most of those in our churches, have been so shaped by the popular culture that no thoughtful preacher can afford to ignore the impact. The combination of music and visual presentation, often highly suggestive, is no longer novel. Casual sexual liaisons are everywhere, not least in many of our churches, often with little shame. “Get even” is a common dramatic theme. Strength is commonly confused with lawless brutality. Most advertising titillates our sin of covetousness. This is the air we breathe; this is our culture.

The maximum flow problem is to find the maximum amount of flow passing from the source node to the sink node. This is equivalent to finding the minimum capacity that needs to be removed from the network so that no flow can pass from the source to the sink. The problem can be formulated as a linear program (LP

On the fourth hand, one reason I don't like IDEs quite so much is that they can make it hard to know when you've actually seen everything. Walking around in a graph, it's hard to know you've touched all the parts. Whereas if you've got some linear order, it's guaranteed to take you through everything.

If machinery is conceived transcendently as instrumental technology it is essentially determined in opposition to social relations, but if it is integrated immanently as cybernetic technics it redesigns all oppositionality as non-linear flow. There is no dialectic between social and technical relations, but only a machinism that dissolves society into the machines whilst deterritorializing the machines across the ruins of society, whose ‘general theory … is a generalized theory of flux’, which is to say: cybernetics. Beyond the assumption that guidance proceeds from the side of the subject lies desiring production: the impersonal pilot of history. Distinctions between theory and practice, culture and economy, science and technics, are useless after this point. There is no real option between a cybernetics of theory or a theory of cybernetics, because cybernetics is neither a theory nor its object, but an operation within anobjective partial circuits that reiterates ‘itself’ in the real and machines theory through the unknown. ‘Production as a process overflows all ideal categories and forms a cycle that relates itself to desire as an immanent principle.’ Cybernetics develops functionally, and not representationally: a ‘desiring machine, a partial object, does not represent anything’. Its semi-closed assemblages are not descriptions but programs, ‘auto’-replicated by way of an operation passing across irreducible exteriority. This is why cybernetics is inextricable from exploration, having no integrity transcending that of an uncomprehended circuit within which it is embedded, an outside in which it must swim. Reflection is always very late, derivative, and even then really something else. (294-5)

In real life, when emotions and sentiments are involved and the very continuity of life is at stake, there are no quantitative theories, linear programming, and applied mechanics available to solve those problems.

Combinatorial complexity is well understood and can be tackled in an engineering fashion. For example, the airline schedule is usually built using a technique called linear programming. In the case of dynamics, changes through time create the complexity, like the anthill we discussed in the first chapter. In this case, engineering techniques may struggle. Dynamic complexity is the topic addressed by systems thinking.

The final, two-way arrow indicates the most subtle and nefarious stage of this neurological programming, the feedback between the incoming energy (plus additions and minus subtractions) and the language system (including symbolic, abstract languages like mathematics) which the brain happens to use habitually. The final precept in humans is always verbal or symbolic and hence coded into the pre-existing structure of whatever languages or systems the brain has been taught. The process is not one of linear reaction but of synergetic transaction. This finished product is thus a neurosemantic construct, a kind of metaphor.

I also employ the "scatter" technique of Sufi writers. Topics do not always appear in linear, "logical" order, but in a non-linear psycho-logical order calculated to produce new ways of thinking and perceiving. This technique also intends to assist the process of "internalization.

Cells are trickier to program than a typical computer, in part because we don’t have a complete understanding of the cell’s machinery, and in part because biology is a water-based technology. This makes it different from technologies that are based on, say, silicon chips and electronics, where electrons whiz around on fixed paths while precise, high-speed switches control the flow. The cell is a vat of soup containing thousands of different molecules, and they are all constantly jiggling around and interacting, but moving very slowly compared to zippy electrons. Cellular processes and code aren’t completely random, but they aren’t linear and logical, either, which makes it difficult to predict exactly how any given biological system will behave. Cells and their components don’t come with owners’ manuals—they lack standards or specifications that would normally help an engineer build a device.

1. A Rich Life means you can spend extravagantly on the things you love as long as you cut costs mercilessly on the things you don’t. 2. Focus on the Big Wins—the five to ten things that get you disproportionate results, including automating your savings and investing, finding a job you love, and negotiating your salary. Get the Big Wins right and you can order as many lattes as you want. 3. Investing should be very boring—and very profitable—over the long term. I get more excited eating tacos than checking my investment returns. 4. There’s a limit to how much you can cut, but no limit to how much you can earn. I have readers who earn $50,000/year and ones who earn $750,000/year. They both buy the same loaves of bread. Controlling spending is important, but your earnings become super-linear. 5. Your friends and family will have lots of “tips” once you begin your financial journey. Listen politely, then stick to the program. 6. Build a collection of “spending frameworks” to use when deciding on buying something. Most people default to restrictive rules (“I need to cut back on eating out . . .”), but you can flip it and decide what you’ll always spend on, like my book-buying rule: If you’re thinking about buying a book, just buy it. Don’t waste even five seconds debating it. Applying even one new idea from a book is worth it. (Like this one.) 7. Beware of the endless search for “advanced” tips. So many people seek out high-level answers to avoid the real, hard work of improving step by step. It’s easier to dream about winning the Boston Marathon than to go out for a ten-minute jog every morning. Sometimes the most advanced thing you can do is the basics, consistently. 8. You’re in control. This isn’t a Disney movie and nobody’s coming to rescue you. Fortunately, you can take control of your finances and build your Rich Life. 9. Part of creating your Rich Life is the willingness to be unapologetically different. Once money isn’t a primary constraint, you’ll have the freedom to design your own Rich Life, which will almost certainly be different from the average person’s. Embrace it. This is the fun part! 10. Live life outside the spreadsheet. Once you automate your money using the system in this book, you’ll see that the most important part of a Rich Life is outside the spreadsheet—it involves relationships, new experiences, and giving back. You earned it.

The cure for linear thinking is process thinking, which focuses on continuous change and learning, rather than just the end result. If you start thinking in terms of what you can learn along the way, and accept that there will be many ups and downs, you will go forward.

The Scheffe test is the most conservative, the Tukey test is best when many comparisons are made (when there are many groups), and the Bonferroni test is preferred when few comparisons are made. However, these post-hoc tests often support the same conclusions.3 To illustrate, let’s say the independent variable has three categories. Then, a post-hoc test will examine hypotheses for whether . In addition, these tests will also examine which categories have means that are not significantly different from each other, hence, providing homogeneous subsets. An example of this approach is given later in this chapter. Knowing such subsets can be useful when the independent variable has many categories (for example, classes of employees). Figure 13.1 ANOVA: Significant and Insignificant Differences Eta-squared (η2) is a measure of association for mixed nominal-interval variables and is appropriate for ANOVA. Its values range from zero to one, and it is interpreted as the percentage of variation explained. It is a directional measure, and computer programs produce two statistics, alternating specification of the dependent variable. Finally, ANOVA can be used for testing interval-ordinal relationships. We can ask whether the change in means follows a linear pattern that is either increasing or decreasing. For example, assume we want to know whether incomes increase according to the political orientation of respondents, when measured on a seven-point Likert scale that ranges from very liberal to very conservative. If a linear pattern of increase exists, then a linear relationship is said to exist between these variables. Most statistical software packages can test for a variety of progressive relationships. ANOVA Assumptions ANOVA assumptions are essentially the same as those of the t-test: (1) the dependent variable is continuous, and the independent variable is ordinal or nominal, (2) the groups have equal variances, (3) observations are independent, and (4) the variable is normally distributed in each of the groups. The assumptions are tested in a similar manner. Relative to the t-test, ANOVA requires a little more concern regarding the assumptions of normality and homogeneity. First, like the t-test, ANOVA is not robust for the presence of outliers, and analysts examine the presence of outliers for each group. Also, ANOVA appears to be less robust than the t-test for deviations from normality. Second, regarding groups having equal variances, our main concern with homogeneity is that there are no substantial differences in the amount of variance across the groups; the test of homogeneity is a strict test, testing for any departure from equal variances, and in practice, groups may have neither equal variances nor substantial differences in the amount of variances. In these instances, a visual finding of no substantial differences suffices. Other strategies for dealing with heterogeneity are variable transformations and the removal of outliers, which increase variance, especially in small groups. Such outliers are detected by examining boxplots for each group separately. Also, some statistical software packages (such as SPSS), now offer post-hoc tests when equal variances are not assumed.4 A Working Example The U.S. Environmental Protection Agency (EPA) measured the percentage of wetland loss in watersheds between 1982 and 1992, the most recent period for which data are available (government statistics are sometimes a little old).5 An analyst wants to know whether watersheds with large surrounding populations have

(e). Hence the expressions are equivalent, as is y = ŷ + e. Certain assumptions about e are important, such as that it is normally distributed. When error term assumptions are violated, incorrect conclusions may be made about the statistical significance of relationships. This important issue is discussed in greater detail in Chapter 15 and, for time series data, in Chapter 17. Hence, the above is a pertinent but incomplete list of assumptions. Getting Started Conduct a simple regression, and practice writing up your results. PEARSON’S CORRELATION COEFFICIENT Pearson’s correlation coefficient, r, measures the association (significance, direction, and strength) between two continuous variables; it is a measure of association for two continuous variables. Also called the Pearson’s product-moment correlation coefficient, it does not assume a causal relationship, as does simple regression. The correlation coefficient indicates the extent to which the observations lie closely or loosely clustered around the regression line. The coefficient r ranges from –1 to +1. The sign indicates the direction of the relationship, which, in simple regression, is always the same as the slope coefficient. A “–1” indicates a perfect negative relationship, that is, that all observations lie exactly on a downward-sloping regression line; a “+1” indicates a perfect positive relationship, whereby all observations lie exactly on an upward-sloping regression line. Of course, such values are rarely obtained in practice because observations seldom lie exactly on a line. An r value of zero indicates that observations are so widely scattered that it is impossible to draw any well-fitting line. Figure 14.2 illustrates some values of r. Key Point Pearson’s correlation coefficient, r, ranges from –1 to +1. It is important to avoid confusion between Pearson’s correlation coefficient and the coefficient of determination. For the two-variable, simple regression model, r2 = R2, but whereas 0 ≤ R ≤ 1, r ranges from –1 to +1. Hence, the sign of r tells us whether a relationship is positive or negative, but the sign of R, in regression output tables such as Table 14.1, is always positive and cannot inform us about the direction of the relationship. In simple regression, the regression coefficient, b, informs us about the direction of the relationship. Statistical software programs usually show r rather than r2. Note also that the Pearson’s correlation coefficient can be used only to assess the association between two continuous variables, whereas regression can be extended to deal with more than two variables, as discussed in Chapter 15. Pearson’s correlation coefficient assumes that both variables are normally distributed. When Pearson’s correlation coefficients are calculated, a standard error of r can be determined, which then allows us to test the statistical significance of the bivariate correlation. For bivariate relationships, this is the same level of significance as shown for the slope of the regression coefficient. For the variables given earlier in this chapter, the value of r is .272 and the statistical significance of r is p ≤ .01. Use of the Pearson’s correlation coefficient assumes that the variables are normally distributed and that there are no significant departures from linearity.7 It is important not to confuse the correlation coefficient, r, with the regression coefficient, b. Comparing the measures r and b (the slope) sometimes causes confusion. The key point is that r does not indicate the regression slope but rather the extent to which observations lie close to it. A steep regression line (large b) can have observations scattered loosely or closely around it, as can a shallow (more horizontal) regression line. The purposes of these two statistics are very different.8 SPEARMAN’S RANK CORRELATION

he will have to write to Alan and tell him that some new instructions will have to be added to the Waterhouse-simulation Turing machine. This new factor is FMSp, the Factor of Mary Smith Proximity. In a simpler universe, FMSp, would be orthogonal to sigma, which is to say that the two factors would be entirely independent of each other. If it were thus, Waterhouse could continue the usual sawtooth-wave ejaculation management program with no changes. In addition, he would have to arrange to have frequent conversations with Mary Smith so that FMSp would remain as high as possible. Alas! The universe is not simple. Far from being orthogonal, FMSp and sigma are involved, as elaborately as the contrails of dogfighting airplanes. The old sigma management scheme doesn’t work anymore. And a platonic relationship will actually make FMSp worse, not better. His life, which used to be a straightforward set of basically linear equations, has become a differential equation. It is the visit to the whorehouse that makes him realize this.

There may be some easy solution right in front of your nose that you keep missing.” Linear programming problems can seem very hard indeed.