High Tech: 02-17-09 Who is Hiring?

Who’s Hiring in Tech? 2009 Numbers So Far

Written by Marshall Kirkpatrick / Full Article here from ReadWriteTech. Only 1 of 3 blogs I read in the High Tech area.

It may be dismal economic times, but some companies are continuing to make new hires in tech and new media. That’s what we track on our Jobwire site and below you’ll find aggregate hiring numbers for the first 6 weeks of the new year.

We last covered aggregate stats in the middle of December and the new numbers are similar to what we saw then. IT and software companies are hiring more than anyone else, but marketing firms are now hiring more than publishing and social media companies, a switch since our last report.

January – Feb. 16th 2009 Hires in Tech and New Media Total reported: 239 Source: readwriteweb.com/jobwire

Lincoln Electric: 02-17-09 Staying Up-to-Date with the Company

Class, some video on Lincoln Electric.

Up to SPEED visits Lincoln Electric

Lincoln Electric Welding Tip by Bryan Fuller

Question: 02-05-09 Class, what happened in Zimbabwe?

HARARE, Zimbabwe (CNN) — Zimbabwe slashed 12 zeros from its currency as hyperinflation continued to erode its value, the country’s central bank announced Monday.

Patrick Chinamasa, Zimbabwe’s acting finance minister, arrives last week at Parliament to present the ‘09 budget.

“Even in the face of current economic and political challenges confronting the economy, the Zimbabwe dollar ought to and must remain the nation’s currency, so as to safeguard our national identity and sovereignty. … Our national currency is a fundamental economic pillar of our sovereignty,” said Gideon Gono, governor of the Reserve Bank of Zimbabwe.

“Accordingly, therefore, this monetary policy statement unveils yet another necessary program of revaluing our local currency, through the removal of 12 zeros with immediate effect.”

The move means that 1 trillion in Zimbabwe dollars now will be equivalent to one Zimbabwe dollar.

Original article.

Barack Obama: 02-04-09 Admits Mistake and Moves On

After two of his cabinet nominees revealed tax problems the President has been forced to admit – in his words – ‘I screwed up’. Sky’s Washington Correspondent, Michelle Clifford, reports.

Thanks President Obama.

Career: 01-30-09 Korn/Ferry, Top 10 Strategies for Job Seekers

Class, if you are looking for a job now, here are some pointers.

Korn/Ferry International (NYSE:KFY), a premier global provider of talent management solutions, today issued 10 strategies for job seekers in today’s turbulent economic climate. The top 10 list was culled and rank-ordered from recommendations provided by Korn/Ferry’s global network of executive recruiters:

10) Start the search immediately: Don’t take extended time off. The search process can take 6-12 months for senior executives.

9) Treat the search process like a job: Establish your schedule and hold yourself accountable for making progress daily.

Be open to interim positions, freelancing or consulting: Companies are cutting fixed costs in today’s economy, but may have consulting opportunities for projects or niche specialties to compensate for reduced head-count. These opportunities enable you to draw an income, maintain your skills and move you to the front of the line when hiring begins.

7) Be willing to commute or relocate: As industries evolve, career opportunities migrate. Know where the positions are going in your field and be willing to move for the right opportunity.

6) Don’t panic, be patient, but don’t be picky: Appearing overanxious to a prospective employer will only diminish your value. And certainly you don’t want to jump from one precarious position to the next. But dream jobs are scarce in today’s market. Remember that most positions are not strictly bound by their job description but rather they are what you make of them.

5) Be flexible: Don’t get hung up on compensation structure and title. Coming in at a pay grade or title below your ideal may work to your advantage. As you exceed expectations, title and salary will adjust accordingly in time.

4) Keep sharp: Stay current on the latest news, trends and technologies that are important in your industry.

3) Stay fit: Don’t neglect your health and diet, which too often lapse with the stress of job searching.

2) Use your resources: There are a number of online tools and free resources that assist job seekers. Start with your university, professional organizations, veterans group or other affiliations you may have for assistance. Online resources recommended by Korn/Ferry executive recruiters include:

• U.S. Department of Labor Employment and Training Administration: http://www.doleta.gov/
• National Career Development Association: http://ncda.org/
• Wall Street Journal Careers page: http://online.wsj.com/public/page/news-career-jobs.html
• About.com / Career Planning: http://careerplanning.about.com/

1) Network, network, network: There’s no substitute for personal relationships when looking for career opportunities. Professional associations, alumni associations, and informational interviews are tried and true ways to conduct a job hunt. Today, networking is easier than ever with social and professional networking sites like LinkedIn, Classmates.com or Facebook. (Wikipedia has an extensive list at: http://en.wikipedia.org/wiki/List_of_social_networking_websites)

“Just like successful organizations, leaders find creative ways to quickly steer themselves in the right direction during uncertain times,” said Bob Damon, President, North America, Korn/Ferry International. “We’re finding that executives who are more aggressive, proactive and resourceful with their job search during this economic turmoil are rebounding much more quickly than those who are passive and waiting for an opportunity to come their way.”

Article here.

Economics: 01-22-09 Lagrange Multipler, A Review

Below is an easy to understand video reviewing Lagrange multipliers.

Modified From Wikipedia

In mathematical optimization, the method of Lagrange multipliers (named after Joseph Louis Lagrange) provides a strategy for finding the maximum/minimum of a function subject to constraints.

For example (Figure on the right), consider the optimization problem

maximize

subject to

We introduce a new variable (λ) called a Lagrange multiplier, and study the Lagrange function defined by

If (x,y) is a maximum for the original constrained problem, then there exists a λ such that (x,y,λ) is a stationary point for the Lagrange function (stationary points are those points where the partial derivatives of Λ are zero). However, not all stationary points yield a solution of the original problem. Thus, the method of Lagrange multipliers yields a necessary condition for optimality in constrained problems.^[1]

Introduction

Consider the two-dimensional problem introduced above:

maximize

subject to

We can visualize contours of f given by

for various values of d, and the contour of g given by g(x,y) = c.

Suppose we walk along the contour line with g = c. In general the contour lines of f and g may be distinct, so traversing the contour line for g = c could intersect with or cross the contour lines of f. This is equivalent to saying that while moving along the contour line for g = c the value of f can vary. Only when the contour line for g = c intersects contour lines of f tangentially, we do not increase or decrease the value of f — that is, when the contour lines touch but do not cross. A familiar example can be obtained from weather maps, with their contour lines for temperature and pressure: the constrained extrema will occur where the superposed maps show touching lines (isopleths).

The contour lines of f and g touch when the tangent vectors of the contour lines are parallel. Since the gradient of a function is perpendicular to the contour lines, this is the same as saying that the gradients of f and g are parallel. Thus we want points (x,y) where g(x,y) = c and

,

where

is the gradient. The constant λ is required because, even though the directions of both gradient vectors are equal, the magnitudes of the gradient vectors are generally not equal.

To incorporate these conditions into one equation, we introduce an auxiliary function

and solve

This is the method of Lagrange multipliers.

Be aware that the solutions are the stationary points of the Lagrangian Λ; they are not necessarily extrema of Λ. In fact, the function Λ is unbounded: given a point (x,y) that does not lie on the constraint, letting makes Λ arbitrarily large or small.

[edit] A more general formulation: The weak Lagrangian principle

Denote the objective function by and let the constraints be given by . The domain of f should be an open set containing all points satisfying the constraints. Furthermore, f and the g_k must have continuous first partial derivatives and the gradients of the g_k must not be zero on the domain.^[2] Now, define the Lagrangian, Λ, as

k is an index for variables and functions associated with a particular constraint, k.

without a subscript indicates the vector with elements , which are taken to be independent variables.

Observe that both the optimization criteria and constraints g_k(x) are compactly encoded as stationary points of the Lagrangian:

if and only if

means to take the gradient only with respect to each element in the vector , instead of all variables.

and

implies g_k = 0.

Collectively, the stationary points of the Lagrangian,

,

give a number of unique equations totaling the length of plus the length of .

The method of Lagrange multipliers is generalized by the Karush–Kuhn–Tucker conditions, which can also take into account inequality constraints of the form h(x) ≤ c.

[edit] Interpretation of the Lagrange multipliers

Often the Lagrange multipliers have an interpretation as some quantity of interest. To see why this might be the case, observe that:

So, λ_k is the rate of change of the quantity being optimized as a function of the constraint variable. As examples, in Lagrangian mechanics the equations of motion are derived by finding stationary points of the action, the time integral of the difference between kinetic and potential energy. Thus, the force on a particle due to a scalar potential, , can be interpreted as a Lagrange multiplier determining the change in action (transfer of potential to kinetic energy) following a variation in the particle’s constrained trajectory. In economics, the optimal profit to a player is calculated subject to a constrained space of actions, where a Lagrange multiplier is the value of relaxing a given constraint (e.g. through bribery or other means).

[edit] Examples

[edit] Very simple example

Fig. 3. Illustration of the constrained optimization problem.

Suppose you wish to maximize f(x,y) = x + y subject to the constraint x² + y² = 1. The constraint is the unit circle, and the level sets of f are diagonal lines (with slope -1), so one can see graphically that the maximum occurs at (and the minimum occurs at )

Formally, set g(x,y) = x² + y² − 1, and

Λ(x,y,λ) = f(x,y) + λg(x,y) = x + y + λ(x² + y² − 1)

Set the derivative dΛ = 0, which yields the system of equations:

As always, the equation is the original constraint.

Combining the first two equations yields x = y (explicitly, , otherwise (i) yields 1 = 0, so one has x = − 1 / (2λ) = y).

Substituting into (iii) yields 2x² = 1, so and the stationary points are and . Evaluating the objective function f on these yields

thus the maximum is , which is attained at and the minimum is , which is attained at .

[edit] Simple example

Fig. 4. Illustration of the constrained optimization problem.

Suppose you want to find the maximum values for

with the condition that the x and y coordinates lie on the circle around the origin with radius √3, that is,

As there is just a single condition, we will use only one multiplier, say λ.

Use the constraint to define a function g(x, y):

The function g is identically zero on the circle of radius √3. So any multiple of g(x, y) may be added to f(x, y) leaving f(x, y) unchanged in the region of interest (above the circle where our original constraint is satisfied). Let

The critical values of Λ occur when its gradient is zero. The partial derivatives are

Equation (iii) is just the original constraint. Equation (i) implies x = 0 or λ = −y. In the first case, if x = 0 then we must have by (iii) and then by (ii) λ=0. In the second case, if λ = −y and substituting into equation (ii) we have that,

Then x² = 2y². Substituting into equation (iii) and solving for y gives this value of y:

Thus there are six critical points:

Evaluating the objective at these points, we find

Therefore, the objective function attains a global maximum (with respect to the constraints) at and a global minimum at The point is a local minimum and is a local maximum.

[edit] Example: entropy

Suppose we wish to find the discrete probability distribution with maximal information entropy. Then

Of course, the sum of these probabilities equals 1, so our constraint is g(p) = 1 with

We can use Lagrange multipliers to find the point of maximum entropy (depending on the probabilities). For all k from 1 to n, we require that

which gives

Carrying out the differentiation of these n equations, we get

This shows that all p_i are equal (because they depend on λ only). By using the constraint ∑_k p_k = 1, we find

Hence, the uniform distribution is the distribution with the greatest entropy.

[edit] Economics

Constrained optimization plays a central role in economics. For example, the choice problem for a consumer is represented as one of maximizing a utility function subject to a budget constraint. The Lagrange multiplier has an economic interpretation as the shadow price associated with the constraint, in this case the marginal utility of income.

[edit] The strong Lagrangian principle: Lagrange duality

Given a convex optimization problem in standard form

with the domain having non-empty interior, the Lagrangian function is defined as

The vectors λ and ν are called the dual variables or Lagrange multiplier vectors associated with the problem. The Lagrange dual function is defined as

The dual function g is concave, even when the initial problem is not convex. The dual function yields lower bounds on the optimal value p ^* of the initial problem; for any and any ν we have . If a constraint qualification such as Slater’s condition holds and the original problem is convex, then we have strong duality, i.e. .

[edit] See also

• Karush–Kuhn–Tucker conditions: generalization of the method of Lagrange multipliers.
• Lagrange multipliers on Banach spaces: another generalization of the method of Lagrange multipliers.
• Constrained optimization and Lagrange multipliers

[edit] References

1. ^ Vapnyarskii, I.B. (2001), “Lagrange multipliers”, in Hazewinkel, Michiel, Encyclopaedia of Mathematics, Kluwer Academic Publishers, ISBN 978-1556080104 .
2. ^ Gluss, David and Weisstein, Eric W., Lagrange Multiplier at MathWorld.

[edit] External links

Exposition

• Conceptual introduction (plus a brief discussion of Lagrange multipliers in the calculus of variations as used in physics)

For additional text and interactive applets

• Simple explanation with an example of governments using taxes as Lagrange multipliers
• Applet
• Tutorial and applet
• Good Video Lecture of Lagrange Multipliers
• Slides accompanying Bertsekas’s nonlinear optimization text, with details on Lagrange multipliers (lectures 11 and 12)
• http://eom.springer.de/L/l057190.htm

CLASS: 01-21-09 Ritz-Carlton Kuala Lumpur

Class, a few videos on YouTube which I found that were related to the Ritz-Carlton Kuala Lumpur

Commercial

From 2006 – Malaysia Airlines and Ritz-Carlton Kuala Lumpur

Also, here is the homepage for the Ritz-Carlton Kuala Lumpur

DELL: 01-21-09 Virtual Manufacturing Tour, Customer Service

Class,

First, a virtual manufacturing tour with Michael

Second, handling a tough customer

Economics: 01-21-09 Coordination In Production

A funny but useful video clip showing the need for coordination between two line workers; it is a simple case, but useful.

Additionally, you can call the Lucy video a problem of synchronization. The output of the workers must be synchronized in some way, but the workers do not need to speak to each other (like Lucy and Vivian do in the video).

Strategy: 01-19-09 Pivot Points, Roles

John Boudreau

Contact Information

Telephone: (213) 740-9814
Voice Mail: (213) 740-4988
E-Mail: john.boudreau@usc.edu

An interview with John Boudreau; he argues that a company should invest in your strategic pivot points–roles where improved performance would make the biggest difference to executing your strategy. John is Professor, Management & Organization, Marshall School of Business and Research Director, Center for Effective Organizations, University of Southern California.