
Discrete Optimization course begins today - corey
https://www.coursera.org/course/optimization
======
xtrumanx
According to a staff reply[0] on the forums, your assignments will only submit
the output of your programs so you can use any language you'd like in the
course. Did anyone do the Scala course that ended last month? This course
seems like the perfect place to try out your newly developed functional
programming skills.

I've watched the preliminary videos and I've got to say this seems like some
really good stuff. If you like programming challenges, you should definitely
sign up for this course. All the assignments and video lecturers are available
from the start so you can study at your own pace but you need to submit your
assignments on time to receive credit.

Also look out for:

\- Algorithms: Design and Analysis, Part 1[1]

\- Coding the Matrix: Linear Algebra through Computer Science Applications[2]

They're both starting in 12 days on July 1st.

[0, You may need to be enrolled and logged in to view this thread]
[https://class.coursera.org/optimization-001/forum/thread?thr...](https://class.coursera.org/optimization-001/forum/thread?thread_id=41&post_id=77#comment-32)

[1][https://www.coursera.org/course/algo](https://www.coursera.org/course/algo)

[2][https://www.coursera.org/course/matrix](https://www.coursera.org/course/matrix)

------
petercooper
Be sure to watch the promo video at the top. It does nothing to dispel the
stereotype that the best academics are borderline crazy.. ;-)

~~~
astrec
Pascal is one of the most enthralling lecturers I've ever been... well,
lectured by. He's incredibly enthusiastic. I think it comes across in his
videos, no? ;)

------
WildUtah
If you like this kind of thing and have a slightly competitive mindset, you
might also like:

[http://www.codeforces.com](http://www.codeforces.com)
[http://www.topcoder.com/tc](http://www.topcoder.com/tc)

------
snake_plissken
I enrolled and I am very excited. The material looks very interesting,
especially since I don't have any formal background in this field and I work
in operations research.

~~~
corey
How do you like working in OR? Are there a lot of jobs in the field? I'm a
math major and like programming, so it seems like it'd be an interesting
career for me.

~~~
astrec
No lots of jobs, no, but there are jobs. It's an interdisciplinary field, so
your math and programming background will hold you in good stead. Some basic
stats are a good idea too.

Of course some people are doing OR but don't know they're doing it. Some just
use another label. Other useful search terms include "management science",
"operational research" (UK), "advanced analytics", and of course
"optimi(s||z)ation".

If you're after an introductory text I can recommend Wayne Winston's
Operations Research: Applications and Algorithms. 4th edition. ISBN-13:
978-0534380588

------
antman
From the lecture: "These are the problems we are going to deal with. If you
find any efficient solutions for all of them call me. And don't tell ANYONE"

FYI, so far it seems that this course requires lot of hours...

~~~
xtrumanx
He mentioned that you may need to invest about 12 hours a week on the
assignments but that figure is more accurate regarding the latter assignments
as they get harder as you go along.

The first week's assignment does not seem that hard after having watched the
lecture but I've yet to submit my assignment.

It's a free course and there's no harm in enrolling and giving it a shot. At
very least, you'll be exposed to some new and interesting stuff. At best,
you'll _master_ some new and interesting stuff.

To me, this seems like the kind of stuff that'll make me "level-up" as a
programmer so I'm pretty excited and enthusiastic right now.

~~~
pyoung
Ugghhh, I signed up for this, my only complaint with coursera is there are way
too many classes I want to take. Am already taking two classes that are about
to wrap up and am probably spending 15-20 hrs/wk on them (lectures included).
Hopefully I can manage the load for the next two weeks.

~~~
azth
Hehe, same here! So many fantastic courses to choose from, not enough time :)

------
kenster07
I, for one, am very appreciative of this course, and the professor for taking
the time to do it. Doing the first week's assignment, I've already learned a
lot about programming for NP-hard problems.

------
graycat
Not too impressed with the course.

My qualifications: I got started in optimization scheduling the fleet at
FedEx. In the words of FedEx founder, COB, CEO F. Smith, my work "Solved the
most important problem facing the start of Federal Express" and the output
from my software was "An amazing document".

Later my Ph.D. research was in optimization and, in part, what I'd wished I'd
known while at FedEx.

Since my Ph.D. once there a problem in 'resource allocation' that became a 0-1
integer linear programming problem with 40,000 constraints and 600,000
variables. I derived some math, wrote some software, and in 905 seconds on a
90 MHz PC got a feasible solution within 0.025% of optimality.

And there have been other problems.

For the video, a good, first suggestion for an attack on a knapsack problem is
via dynamic programming.

By a wide margin, the most important topic in optimization is the simplex
algorithm for linear programming. While the algorithm directly solves many
problems in practice, it is the most important tool in solving optimization
problems that are not linear programming. Usually there the role of linear
programming is as a local linear approximation in an iterative scheme of
better approximations.

Much of the reason for the power of linear programming as a tool in larger
iterative schemes is the several ways can take a linear programming problem
and an optimal solution for it, make some changes in the problem, start with
the optimal solution for the old problem, and get an optimal solution for the
new problem very quickly.

Good starts in linear programming include:

Mokhtar S. Bazaraa and John J. Jarvis, 'Linear Programming and Network Flows',
ISBN 0-471-06015-1, John Wiley and Sons.

Vavsek Chvatal, 'Linear Programming', ISBN 0-7167-1587-2, W. H. Freeman, New
York.

Yes, the min-cost capacitated (each arc has a maximum flow it can carry)
network flow problem, a special case of which is the transshipment problem
(find the least cost way to ship some one good from factories to warehouses;
also the source of the Kantorovich Nobel prize in economics) is a linear
programming problem, and there the simplex algorithm simplifies and becomes
the network simplex algorithm. Curiously, and of importance for fast
computation, in the network simplex algorithm a basic feasible solution
corresponds to a spanning tree in the network. For that problem, don't miss
the W. Cunningham idea of a 'strongly feasible basis' or the more recent
polynomial algorithm of D. Bertsekas.

A significantly large fraction of real problems in combinatorial and discrete
optimization can be solved as min-cost capacitated network flow problems via
the network simplex algorithm. A big reason is, if the flow capacities are all
integers and if have an integer basic feasible solution, then the simplex
algorithm maintains integer basic feasible solutions for no extra work and,
with the usual assumptions, produces an optimal integer basic feasible
solution. So this is a case of linear programming where get integer linear
programming for no extra effort; really, since nearly all the arithmetic is
just integer, don't have to be concerned about roundoff error and get the
solution for less effort.

Yes, in combinatorial and discrete optimization, along with the rest of
optimization, linear algebra helps.

Curiously, one of the more powerful approaches to combinatorial optimization
is a technique from nonlinear programming called Lagrangian relaxation, and
there commonly the main computational step is linear programming. With that
technique, some nonlinear duality theory can yield some bounds on how far are
away from an optimal solution; such duality theory is what yielded the bound
of 0.025% above.

For combinatorial optimization there is, say,

George L. Nemhauser and Laurence A. Wolsey, 'Integer and Combinatorial
Optimization', ISBN 0-471-35943-2, John Wiley & Sons, Inc., New York.

William J. Cook, William H. Cunningham, William R. Pulleyblank, and Alexander
Schrijver, 'Combinatorial Optimization', ISBN 0-471-55894-X, John Wiley &
Sons, Inc., New York.

In the OP, the emphasis on NP-complete and algorithms that are worst case
exponential is a bit exaggerated. E.g., as in the work of Klee and Minty, the
simplex algorithm is worst case exponential, but in expectation in practice it
is low order polynomial -- for why, there was some good work by K. Borgward.

For integer, combinatorial, and discrete optimization, I have yet to see a
claim that getting approximately optimal solutions is in NP-complete. In
particular, if in a real problem have saved $10 million and have a bound
saying that can't save more than another $2000, then why spend millions trying
to save the last tiny fraction of the last penny and guarantee to have done
so? If are willing to sacrifice the last penny, then it is not so clear (so
far at least to me) that really are being forced into exponential algorithms,
either average or worst case.

The claim in the OP that combinatorial and discrete optimization are part of
computer science looks like a case of 'academic theft'! Such optimization has
long been regarded as part of applied math and there part of operations
research. The material has been of interest in, say, departments of
mathematical sciences. Some parts of optimization, e.g., control theory, have
been regarded as parts of electronic engineering. At times it does appear that
academic computer science wants to regard every topic that makes heavy use of
computing as part of computer science! It looks like computer science has
gotten tired of just quicksort, AVL trees, BNF, YACC, and RDBMS! :-)!

The claims in the OP about the importance of optimization in the economy are a
bit over enthusiastic. Maybe the importance would, could, and should be there,
and maybe in Australia they are there, but my experience in the US is that
there are essentially no significant non-academic career opportunities in
optimization. E.g., at one time near year 2000, I had a nice, long talk with
one of the leading US headhunters in technical fields, and he finally
confessed that in the previous year in all of the US there had been exactly
one recruiting request in operations research. He "packed it in" (from the
movie 'The Sting', i.e., gave up) and went to law school!

Here is a good future -- although a wildly long long shot -- for integer,
combinatorial, and discrete optimization: At present, all but quite simple
problems in such optimization are solved by some experts attacking each
problem one at a time, exploiting special structure of each problem, trying
various techniques, and, hopefully, finally getting some good results. It's
custom, one-off, bench scale, expert, creative, hand work and often, really,
some applied research.

In addition, there have long been programs that can be used to pose or enter
problems in integer, combinatorial, and discrete optimization suitable for
solution by a 'solver'. The problem is, since the programs are general
purpose, the 'solvers' to be used are also general purpose and, without the
hand work, too often in practice are not powerful enough.

Presumably a polynomial algorithm of low degree that showed that P = NP could
be the core of a general purpose solver powerful enough to make solving such
optimization problems easy and routine.

Lacking such a solver, in practice patience with such optimization has long
been a bit thin.

So, what the heck happened? In much of physics, say, planetary motion, we can
write down some equations the real system must solve. If the equations have a
unique solution, then that solution must be the right one for the real system.
At times since early in WWII, such physics was of enormous value for at least
the US DoD.

So, given such an equation, solve it. This technique often worked for, say,
initial value problems for relatively simple ordinary differential equations.
Alas, for the partial differential equations of fluid flow, the Navier-Stokes
equations, the situation was usually a bit challenging.

Also in problems for the US DoD, e.g., 'logistics' or how to move a large army
across a large ocean in least time within capabilities of existing resources,
could write down some equations and other mathematical specifications that a
'best' solution had to satisfy. So, then, to say how to move the army, just
'solve' the equations, etc. This work was called 'operations research', and at
times the US DoD was very enthusiastic about it. With enough US Federal
Government research grants to academics, parts of academics got enthusiastic
about it. And so did Bell Labs.

But there was no law of the universe that said that finding solutions to such
mathematical specifications would be easy. Yes, Navier-Stokes is another
example of difficulty. Now, in cryptography, so is the fundamental theorem of
arithmetic, that is, factoring a positive integer into a product of prime
numbers.

Clay Mathematics Institute in Boston has more than one prize of $1 million
available for progress on such things.

Net, it does very much appear that for now making money by building a popular
Web site and selling ads on it is a much easier way to make money! And, at
times, in such work some applied math, possibly new, can be an advantage. But,
clearly for now, the grand goals of integer, combinatorial, and discrete
optimization are too difficult for 'prime time' in practice in mainstream
business.

Or, problem A can make a lot of money if we have a lot of applied math and
computing. So, let's attack problem A. Alas, that much applied math and
computing, or even just part of the computing, may be much more valuable for
solving other problems!

~~~
wavesounds
You really wrote the algorithm for FedEx? Thats amazing!

I read your whole post but I'm not sure exactly what you don't like about the
class other than you seem to think learning Discrete Optimization in general
is a waste of time because theres more lucrative problems much easier to
solve.

Care to briefly elaborate on what you dislike about this course specifically
(it does appear that Simplex is covered)?

~~~
graycat
Part I

I wrote quickly and was not very clear for people without good backgrounds in
optimization.

> You really wrote the algorithm for FedEx? Thats amazing!

In the sense of business, it actually was "amazing": Likely it saved the
company from going out of business. Too many Members of the Board of Directors
were concerned that there could be no suitable schedule. So, one evening Roger
Frock and I used my software to develop a schedule for all planned 33
airplanes and all planned 90 US cities.

Printed out, FedEx founder, COB, CEO F. Smith said at the next senior staff
meeting "Amazing document. Solved the most important problem facing Federal
Express.".

Two guys representing Board Member General Dynamics went over the schedule and
announced "It's a little tight in a few places, but it's flyable.". Then the
Board was happy. As I recall, $55 million in funding was enabled, not all
equity.

So, the software was "amazing" just as business. But as applied math, the
software was junk! Work for the real, pressing, short term business problem?
Yes. Really optimization? No!

But I did continue and saw that what I should do was (1) generate some
thousands of 'feasible' trips from Memphis and back and for each carefully add
up the direct costs, (2) set up a 0-1 integer linear programming problem with
one row for each of the 90 cities and one column for each of the thousands of
feasible trips. Then each row says to serve some one city. Each variable, 1 or
0, one for each column, says to use that trip or not. Right: It's 0-1 integer
linear programming set covering, yes, in NP-complete.

So, that's a linear program with only 90 rows. Just as a linear program,
that's promising, likely easy.

Make money? My only competition was a guy with a map of the US on a sheet of 8
1/2 x 11" paper. He didn't have a reasonable way even to add the costs of a
candidate schedule or print it out. So, yes, my work should have saved a
bundle.

If nothing else, just solve the LP without the 0-1 constraints (with only 90
rows, that should be easy), look at the results, and see if have an easy, not
very expensive way to patch up the fractions to 0-1 by hand. Else do a little
branch and bound. If attacking the whole problem for the whole country is too
difficult, then attack it for parts of the country separately; e.g., what do
in the East has next to nothing to do with what do in the West; the goal is
not to announce that have the 'optimal' solution down to the last fraction of
the last penny; instead, the goal is to save money. Don't be embarrassed about
not guaranteeing to save the last $1000 or the last $1,000,000 a month;
instead be just horrified about not saving the first $10 million a month.

Also do a little duality theory to get a bound on how much more money might be
saved. When have saved the first $10 million and have no more than another
$1,000 to save, take the best feasible solution so far and quit.

About then I'd gotten on an airplane and run off to chat about optimization
with G. Nemhauser, then still at Cornell, J. Elzinga, then still at Hopkins,
and J. Pierce, then in Cincinnati, got a stack of books and papers, etc.

There was also another problem: Our fuel prices and availabilities varied
widely by airport. So, a question was, how much fuel to buy at each stop? So,
buy extra fuel where it is cheap and available. This is called 'fuel
tankering'. But, yes, will burn off some of the extra fuel carrying it.

How much extra fuel is burned off is fairly sensitive to the vertical flight
plan used; so also need to select vertical flight plan in a coordinated way.
The problem is also complicated by the fact that package pickup loads are not
known until the plane arrives for the pickup, and those loads will affect the
fuel burned for each candidate vertical flight plan. Then those loads will
also affect how much fuel can be carried. Also relevant is that the fuel
burned and flight time for a vertical flight plan and a load are affected by
essentially random winds, air traffic control, and flying around summer
thunder storms. Also really get charged not just for fuel but also time on the
engines. So, it was complicated.

Now, try to find a way in practice to advise the pilot on what to do? So,
right, it's another optimization problem -- partly discrete, nonlinear,
sequential, stochastic. So, right, stochastic dynamic programming. Doable?
Actually, yes, quite doable. On a computer today, could solve the problem for,
say, five stops, with weight discretized at 100 pounds in about the time it
takes to get a finger off the Enter key or the mouse button.

Also for the vertical flight plans, I went to MIT and chatted with M. Athans
about deterministic optimal control theory.

~~~
graycat
Part II

There had been another place I'd saved the company from going out of business:
Our two representatives from Board Member General Dynamics (GD) had packed
their bags and were on their way back to Texas, which would have killed the
company, when Roger Frock gave me a call and I went to the Board Meeting and
explained some revenue projections I'd done with M. Basch. The GD guys were
happy; the GD check was good; and the company was saved again.

But my offer letter promised founder's stock, and so far I had no stock. My
wife was still in her Ph.D. program at Hopkins. Our home was still in
Maryland, and I was flying jump seat home each few weeks. Also my computer
access, PL/I on VM/CMS, no doubt by a wide margin the best computing then
available for such work, was good in Maryland but sucked in Memphis so that
for the software I had to be in Maryland which torqued one guy (not Smith) in
Memphis. Also, Smith was not really happy about it.

I wanted a piece of paper, stock or Ph.D. On my last day Smith said "You know
if you stay you are in line for $500,000 in Federal Express stock.". He wasn't
putting that in writing; before I joined I was told by an SVP that I'd get the
stock in "two weeks", and that was already too optimistic by over a year; I
didn't know how serious Smith was; I didn't know if the Board would go along;
I wasn't sure how much software I'd have to write for the optimization I had
in mind or how patient Smith would be as I wrote the software and tried this
and that in the optimization; and I was not sure how happy Smith would be
about the likely considerable computer charges I'd run up.

But there was money to be saved: I'd written the first version of the software
totally ASAP, fingers flying over the keyboard. There were some simple tweaks
that could have helped save a lot of money, likely right away enough to pay
for the computing I needed for the optimization. And in the optimization work,
some early results, e.g., just the careful cost calculations, could have saved
much more money than I needed for the optimization. And I believe that there
was a fairly easy way to do the fuel buying problem to get it saving money
quickly. The money to be saved just from my typing in some software was
astounding. Actually, from what I learned later in graduate school, the
optimization should have been not too difficult and saved a bundle, enough to
make a major difference in the bottom line of FedEx.

But Smith wasn't putting the stock in writing, and my wife was in Maryland.
So, I left and got a Ph.D. in optimization.

> I'm not sure exactly what you don't like about the class

I watched the preview lecture.

(1) The emphasis on the knapsack problem is misleading for practice -- really
mostly contrived. For practical problems that really are knapsack problems,
the technical fact that the problem is in NP-complete is not very important;
among the NP-complete problems, in practice knapsack problems are among the
easiest to solve; the usual recommended approach is via dynamic programming.
The claim that knapsack problems encounter exponential running time is over
emphasized to unrealistic.

The professor was over hyping the material in ways that are misleading.
Bummer.

This stuff about NP-completeness is too often used in ways that are totally
misleading in practice. Basically some professors are 'bloviating', trying to
impress people with how difficult their work is.

Such hype can be seen as an attempt to intimidate others, and one cost can be
that others get resentful and just decline to get involved with optimization.
Related is the long, common emphasis on 'optimal' as if saving the last penny
was some high moral objective worth much more than one penny; that emphasis
was, again, a way to intimidate others and, thus, cause optimization projects
to be neglected. The OP is falling into those old traps. Bummer.

Such nonsense goes back to the cartoon early in

Michael R. Garey and David S. Johnson, 'Computers and Intractability: A Guide
to the Theory of NP-Completeness', ISBN 0-7167-1045-5, W. H. Freeman, San
Francisco, 1979.

where the optimization guy says to the business manager that he (the
optimization guy) can't solve the manager's problem but neither can a long
line of other optimization experts. Nonsense, 99 44/100% total, made up, flim-
flam, fraud nonsense. Why? The business manager likely cared essentially only
about saving the first 90% of the cost savings from an 'optimal' solution,
nearly always in practice, and for the rest was quite willing to f'get about
it; what he wanted was likely quite doable; and nearly all the difficulty the
optimization guy was talking about was for the parts the business manager was
willing to f'get about. Really, the optimization guy was not looking to solve
the business manager's problem but looking for a lifetime job pursuing
academic prestige at the business manager's expense. The OPs emphasis on the
difficulty of his work is coming way too close to this old mistake.

E.g., at a start up in Texas, I mentioned, as in my first post in this thread,
I'd gotten a feasible solution within 0.025% of optimality for a 0-1 integer
linear program with 40,000 constraints and 600,000 variables in 905 seconds on
a 90 MHz computer. Then the group of people I was talking to, heavily from
SMU, flatly refused to believe my statement; they were convinced that due to
NP-completeness theory I had to be lying. I was telling the exact truth, and
NP-completeness theory in no way contradicts what I said. NP-completeness
theory is about exact optimality, down to the last tiny fraction of the last
penny for worst case problems, the worst case that can exist even in
principle, and that context is a very long way from using optimization to save
money in practice. Sure, it might be super nice and valuable to have a fast,
low degree polynomial algorithm that shows that P = NP, but lack of such an
algorithm does not say that our optimization problems are too difficult in
practice, especially if all we want to do is save millions of dollars and are
willing to sacrifice the last 10% of the savings.

I remember when I was at FedEx and thinking of going to Brown for my Ph.D. I
visited the campus and ate lunch with two professors, one who was eager for me
to come and the other just the author of a text I'd long since read carefully.
When asked what I was doing at FedEx and explained the fleet scheduling, the
text author responded with contempt "the traveling salesman problem" as if the
work was hopeless. No, not in any very meaningful sense. The goal was to save
money, and that was quite doable, NP-completeness theory or not. That he
wanted to use some tricky point about NP-completeness theory as an excuse not
to save a significant fraction of the FedEx costs, millions a year, was a
major factor in my not going to Brown. We have to wonder how that professor
even tried to get from home to lunch that day since he believed that to do so
he had to solve the traveling salesman problem.

The OP's emphasis on NP-completeness to claim how difficult were the problems
he was solving was nearly as objectionable. He was being misleading. Bummer.

Again, nearly always (sure, if the problem is SAT, then an approximate
solution may be of no interest) the goal in practice is to save money; the
difficulty of saving the last penny, always, worst case, guaranteed, is no
reason not to save the millions that can be saved in nearly all practical
cases for likely quite reasonable effort and possibly some astounding ROI.

Net, the NP-completeness theory is far too often used to claim that such
optimization is "hard", but for saving a lot of money in practice that's often
just wildly false.

Indeed, as I mentioned in my first post in this thread, we are not afraid to
use algorithms that are worst case exponential because simplex is worst case
exponential. To show just how far from reality NP-completeness theory is, as I
mentioned, on average in practice simplex is low order polynomial.

(2) The claim by the OP that if can solve one NP-complete problem with a
'good' algorithm, then can solve them all is, sure, true in principle and nice
to know but not very important in practice and nothing to emphasize in that
introductory video. Here the OP was hyping his work in a misleading way.
Bummer.

(3) The OP's claims that optimization is a big deal in practice are hype and
misleading. Bummer.

The problem with optimization playing a big role in practice was illustrated
there at FedEx: Smith had some huge reasons to have me pursue the
optimization. He didn't support my work nearly well enough, and the main
reason was that he just didn't have faith that he should make that part of his
company the work of some technical experts doing work he didn't understand
(read that statement several more times and fill in with what we can expect
from emotions, ego, sense of control, Smith's pride in the paper he wrote at
Yale, possibly some resentment for academics, his family fortune he'd
invested, his long time associates he'd wanted to count on, promises he'd made
to various people, his image before the 'suits' on his Board, etc.). Law and
medicine have such professional respect; optimization does not.

~~~
graycat
Part III

In the end, it's super important to be the guy who OWNS a business and SELLS
the results. E.g., for optimization, maybe develop the software for free, show
the results and the savings, and then ask to get paid a fraction of the
savings. Let's see, long ago one commercial airline was spending $89 million a
month on jet fuel. I can believe $200 million a month now, also for FedEx.
From a fast Google, get to

    
    
         http://www.transtats.bts.gov/fuel.asp
    

with data for US airlines

    
    
         2013 April 847.5 2,432.4
    

or 847.5 million gallons costing $2,432.4 million dollars in April, 2013.

Save 15% of $200 million a month and get paid 20% of the savings and get paid
$6 million a month, from just one customer. And it's an easy sale: Take case
A, what they are doing now, and cost it out. Then take case B, from
optimization. Then compare costs. Simple. Compelling. Maybe not still
compelling now, but would have been for much of the history of FedEx.

But, for my doing an in-house effort, Smith didn't take the work very
seriously. Right, in the next year I might have burned off $50,000 in VM/CMS
time sharing computer charges. Right. But jet fuel is expensive, too.

> it does appear that Simplex is covered

Yes, of course, the course will have to, but the introductory lecture didn't
emphasize that.

In a sense, simplex is dirt simple -- just elementary row operations very much
like in Gaussian elimination but, using the 'reduced costs', selected to make
money at each iteration in, if you wish, a 'greedy' way. But there's more,
e.g., some surprising points about moving along edges of a closed convex set,
from an intersection of finitely many closed half spaces, from extreme point
to extreme point. For the course, discrete and combinatorial optimization,
really should know simplex quite well; it promises to be the core of the
course. Also, again, simplex is worst case exponential!

For the career prospects of the course, only a tiny fraction of college
courses have good, immediate, direct career contributions. So, I can't be
offended that the OP's course does not have really good career contributions.
But I am offended that the OP tried to claim that his optimization was so
important that there would be good career prospects. Sure, Bixby (of C-PLEX)
bought a nice house in Houston, but mostly I'm still looking for the yachts,
or even the nice houses, of optimization experts. Heck, even job ads would be
reassuring.

I have one of the best Ph.D. degrees in optimization, and it has been
essentially useless for my career. The core reason is, the business guys with
the projects and budgets don't understand optimization, have no respect for
it, and don't want to bet part of their careers on it. There's usually little
or no downside for ignoring optimization. For pushing a project in
optimization and failing, there's a lot of downside. For being successful,
there will likely be resentment, attacks from other managers who feel
threatened, etc. and otherwise no great upside. So optimization projects are
about as popular as a skunk at a garden party.

One final war story: Long the dean of engineering at MIT was T. Magnanti, an
expert in optimization. Once he gave a Goldman Lecture at Hopkins on
optimization of the design of large IP networks. From some old Bell Labs data
(from some work likely close to the book with the cartoon), optimization
should be able to save ballpark 15% of network capital expense; worthwhile
money if can get it.

So, at one time there was a start up in Plano, TX attacking this problem. At
the time, so was I. So, the TX people flew me down for an interview. They had
some venture funding, and it may be that some of the people who met me were
the venture partners. The company's main optimization guy from SMU had just
bowed out. The CEO was a former IBM guy, and they flew me down partly because
of my role at FedEx and also because I'd been at IBM's Watson lab.

So, I arrive. I'm met at the door by the CEO, the IBM guy. Right away he
scowls, and I never see him again. Why? Because I didn't know his name (I'd
had no communications with him), and my handshake was not impressive enough.
He desperately needed some good expertise in optimization, e.g., in a back
room had a high end PC with a copy of C-PLEX gathering dust while his people
were trying total enumeration, but he wanted nothing to do with me. I'm not
that hard to take, not even while tired from a plane trip and driving from the
Dallas airport to Plano.

The point was, he was convinced that his IBM white shirt and IBM salesman
handshake were what were crucial for his company and that my background in
optimization was, well, whatever but likely not really good like a handshake.
He had no respect for optimization. Soon he was 'promoted' to just a Board
slot.

My background in optimization? Did I mention Goldman? He was the Chair of the
committee that approved my Ph.D. research. On the committee was C. ReVelle,
world expert in optimization for facility location (mentioned by the OP). Also
on the committee was J. Cohon, world expert in multi-objective optimization
and long President at CMU. My research was from a suggestion in three words by
G. Nemhauser, world expert in optimization. One paper I'd published solved
some long outstanding questions in the Kuhn-Tucker conditions in optimization
and solved a problem stated in the famous paper by Arrow, Hurwicz, and Uzawa.
I went through one of the best Ph.D. programs in optimization on the planet.
Still, the CEO in TX wanted nothing to do with me. And, really, after my
Ph.D., neither did FedEx.

When I left FedEx, I'd saved the company twice and for more had identified,
formulated, done good first-cut progress on, and presented the three
optimization problems, fleet scheduling, fuel buying, and vertical flight
planning. All I needed was a little consulting money and a good VM/CMS time
sharing account from my home in Maryland, but that was not enough to get Smith
impressed. So, I lost, and so did Smith and FedEx.

In business, optimization is a Rodney Dangerfield field -- it "don't get no
respect". So, if exploit optimization, then do it for your own company or sell
just the results based on the savings obtained: Since the 'suits' are
convinced that optimization can't save much, when the contract is signed they
will believe that they won't have to pay much. When they have to pay $6
million a month, they will be surprised and pleased by how much they are
saving but bitter and furious at the $6 million a month they are signed up to
pay.

There is a secret in business: To get paid well without too much resentment,
jealousy, etc. from others, get paid in ways that others don't really know how
much you are making. So, if some big company has to pay you $6 million a
month, even if you are saving them $30 million a month, they likely will be
torqued; but if can get the $6 million a month from several ad networks from
running many millions of ads, then no one will get torqued.

~~~
amhey
graycat - remember this is a 7-week course and the lecturer, Pascal Van
Hentenryck, in the initial video is stirring up interest imitating Indiana
Jones - he's on a quest, he's passionate. So it's not the Hillier and
Lieberman textbook approach to Operations Research of old. The whole point was
to make the difficult problem of combinatorial problem solving fun and
attractive to a wide audience.

I agree with you that many managers haven't a clue what to do with an OR
graduate. So it does depend on how you position yourself. One reason for
taking the course is to be able to promote your skills passionately (try
making a video of your skills in every day parlance like the introductory
video to this lecture) using current terminology - which as Prof Van
Hentenryck says are in demand at INFORMS conferences (go to the analytics
conference - rather than the main conference which is more academic).

I agree with you that many execs haven't a clue what optimization can do - or
care - in fact most managers satisfice. That's why it's important to find the
right boss who does value your accomplishments. It's also important to update
how you state your value to others - which is one reason I'm doing the course.
I hope you stick with it - and I'd be interested to know what you think at the
end of it.

Is the point of your argument that the leaderboard ranking forces people to
try for an optimal solution, when in a business situation a solution at 99% of
the value might be good enough?

~~~
graycat
The Indiana Jones take off is fine. Mentioning the knapsack problem is less
good because it's not so important in practice. Saying that the knapsack
problem is difficult to solve, e.g., encounters exponential algorithms because
technically it's in NP-complete, is next to irrelevant for practice,
misleading, and hype and not fine.

> it's also important to update how you state your value to others

On this, I outlined my suggestion: Own a little company and sell results based
on how much money they save the customer. Make the sale about saving the
customers money in ways that even an auditor can confirm are correct.

INFORMS is clearly an echo chamber, people in optimization looking for work
and talking to themselves.

Broadly for optimization in business, there is a very serious problem:
Optimization is not a 'profession' like law, medicine, or major parts of
engineering. So, there is no licensing, certification such as the CPA, peer-
review of practice, legal liability, etc. So, as I said, the field "don't get
no respect". Also missing is a point the legal profession has: Any working
lawyer must report only to a lawyer; the interface between the optimization
guy and the business guy is nearly impossible.

> Is the point of your argument ...

I tried to make several points. One of the points was about 'optimal'. The
mathematical definition is fine, but long that definition was taken as
suggesting that what we should do in practice is look for such solutions, then
strain to find them, etc. That turned out to be a grand mistake.

Why? Because maybe there is, compared with what the customers is doing now,
$10 million to be saved with an optimal solution. But too commonly saving all
$10 million is too difficult for the algorithms and computing. So, straining
to save all the $10 million converts an important business problem into a much
more difficult mathematical problem. It also turns out that commonly it's
fairly easy to save, say, $9 million. The difficulty is saving the last of the
money, and the most difficult money to save is the last, say, the last 10
cents.

'Optimal' was taken as a moral objective, as I said, as if saving the last 10
cents was worth much more than 10 cents.

Struggling over 'optimal' taken literally and, thus, making real problems much
more difficult than necessary, was several torpedoes below the waterline of
the ship of optimization.

Part of this mistake was the simplistic and excessive emphasis on NP-
completeness -- for real problems the whole P versus NP question is next to
irrelevant. One way to see this is the simplex algorithm -- it's the core of
optimization and astoundingly fast in practice but worst case exponential.
There is a polynomial algorithm for linear programming, but it's way too slow
in practice. In practice, that an algorithm is worst case exponential is
commonly just irrelevant.

I had to conclude that for business, optimization is a dead field. It got
started due to WWII and US DoD funding, and maybe in places there is still
some interest for US DoD problems.

Here is a little: A post above, in response to a post of mine, claimed that
IBM had a good optimization group. If so, then good for IBM. But I was at
IBM's Watson lab, published a paper on optimization, and off and on considered
joining the optimization group there. Phil Wolfe, William Pulleyblank, Ellis
Johnson. and others were in that group. At one point, Roger Wets was visiting.
The group did the IBM Optimization Subroutine Library (OSL). Then in 3 years
near 1994, IBM lost $16 billion. Johnson joined George Nemhauser at Georgia
Tech. Pulleyblank became a professor at West Point. Basically the optimization
group fell apart. Maybe they put a group back together, but losing Johnson and
Pulleyblank were big mistakes.

E.g., again, with Pulleyblank at West Point, the US DoD remains interested in
optimization.

Heck, I supported myself and my wife through our Ph.D. degrees by working in
optimization for the US DoD.

In academics, the professors were to do research to get the field going, e.g.,
research in 'systems analysis', 'mathematical sciences', 'civil engineering',
'production', etc. Yes, if optimization problems were easy to solve, then
optimization would have central roles in those fields. Alas, mostly important
practical optimization problems are not so easy to solve, even approximately.
So, the professors are still doing research -- maybe in some decades or
centuries they will have something of serious importance for those fields. I
doubt that the research is very well supported.

I tried to give a summary of essentially a 'cultural contradiction' expecting
optimization to be a popular field in business: By the time computing is ready
to make optimization easy enough, there are other things to do with the
computing making much more money than with optimization.

It's not that optimization can't save money in business; there is money to be
saved; in a lot of stable businesses, optimization can provide some of the
highest ROI available to the business. So, there is some ground available
there, what is in principle some fertile ground. So, there can be some
optimization groups here and there. If the course prof has such a group in
Australia, good for him. With some really impressive 'cases' published in,
say, INFORMS, maybe mainline business will try optimization again. I doubt it,
but maybe. Don't hold your breath waiting; there are lots of impressive cases
long since published in INFORMS, and ORSA, _Mangement Science_ , etc. The
optimization literature is huge going back to the late 1940s, e.g., for
Dantzig at Rand and Berkeley.

Here's a little on the difficulty: In the US there are college accrediting
groups, and for some years they said that an undergraduate degree in business
should have courses in optimization and statistics. So, for years each
business school student, undergraduate or MBA, got a course in optimization.
For some years, I taught such courses. Still the field didn't take off.

I can't recommend that anyone try to have a career in optimization in
business. You stand to have an easier time supporting a family with a career
as a plumber, literally. With software, do an information technology start up,
sell out, and pocket, say, $10 million -- knocks the socks off optimization.
With irony, if interested in 'optimization' of your career and financial
security, then avoid optimization!

Optimization is like some item at Dunkin Donuts that doesn't sell. Lots of
other stuff at Dunkin Donuts sells really well, but that one item just
doesn't. They can do a good job getting the item ready to eat, put it out in
the display cases, and wait, and what happens is the item just sits there and
goes stale. Then they throw away the stale, unsold items. It was a waste.
Finally, Dunkin Donuts just quits offering the item.

Dunkin Donuts doesn't go on and on about why the item really should sell.
Instead, they just listen to the clear message they've gotten from the market
and, thus, save having to figure out solid reasons it doesn't sell.

Similarly all across business -- some stuff doesn't sell or doesn't sell very
well or sells only a little and then only into a tiny market. Optimization in
business is like that -- at best, it's a super tough sale; usually it just
doesn't sell.

Optimization, as a field, in business, is a dead duck. F'get about it and
pursue something else.

------
wavesounds
I love how animated and excited this guy is about his subject!

