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Applied Mathematical Programming (1977)

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Re: Applied Mathematical Programming (1977)

#21
post #19

"Applied ..."? On the Optimization Ph.D. qualifying exam, got a "High Pass" and the best score in the class. In optimization answered a question in the Kuhn-Tucker constraint qualifications and had the paper accepted quickly in Mathematical Programming . Taught linear programming in a well-known business school for 5 years. NYC had a few users, in a loose group, of linear programming but were not very good at it. Som…

Bro, you spend a really long time getting great at this and from the bottom of my heart I feel your pain and believe we need to improve a lot as a society to take advantage of talents like you better.

Thanks for investing so much time to it, I share your sadness for your wife and hope you get ao much good as you tried to bring to the world, man

Re: Applied Mathematical Programming (1977)

#22
post #19

"Applied ..."? On the Optimization Ph.D. qualifying exam, got a "High Pass" and the best score in the class. In optimization answered a question in the Kuhn-Tucker constraint qualifications and had the paper accepted quickly in Mathematical Programming . Taught linear programming in a well-known business school for 5 years. NYC had a few users, in a loose group, of linear programming but were not very good at it. Som…

> A fact of life: Nearly always people with money, power, and optimization problems don't understand optimization, fear and resent those who do, and choose just to avoid the subject.

Worth considering this hard question: who optimized their life better? Them or us?

Re: Applied Mathematical Programming (1977)

#23
post #19

"Applied ..."? On the Optimization Ph.D. qualifying exam, got a "High Pass" and the best score in the class. In optimization answered a question in the Kuhn-Tucker constraint qualifications and had the paper accepted quickly in Mathematical Programming . Taught linear programming in a well-known business school for 5 years. NYC had a few users, in a loose group, of linear programming but were not very good at it. Som…

You’re attributing these life challenges to your STEM PhD speciality? I am sensitive to your hardship, but optimization has nothing to do with it. People are succeeding with bachelor’s degrees in Latin.

The point of the post is that in my long experience there was not much about optimization that in any significant practical career sense was "applied", i.e., no jobs even to keep one from living on the streets, far from a career to buy a house and support a family.

The point about my Ph.D. with a lot in optimization is that I was quite well qualified in the field, but even with all those qualifications "applied" was not real, i.e., there just was nothing like a career in optimization applications.

Can suspect that, yes, "Ph.D." did seriously damage all career prospects, optimization, math, even computing.

The successes I did have were in computing. At the time the math was a small aid.

Did get paid for some work in applied optimization on some military problems. Looking back, there was some stranger in the office eager to discuss the weather, etc. with me. Maybe I said the wrong things about some of the US foreign wars, and then the stranger was gone. Might have been some high end military job interview that needed only gung ho attitudes toward foreign policy.

Delicate political situation, and I was oblivious about politics. Like, "stick to two subjects, the weather and everyone's health" and avoid "sex, politics, and religion".

Early on while I was in a grad program teaching math, some recruiters came from DC desperate for anyone with some math/physics education. I interviewed: Got a offer and took a job right away. Soon bought a new car and got married.

In those days, around DC, to get a job, just look in the WaPo, apply, go on the interview, show some knowledge of some of computing, get an offer, compare a few offers, with, say, a 15% raise, and accept one -- worked great. For a while, at GE time sharing national HQ, was the main guy for the applied math library, e.g., the FFT, regression analysis. Later a good background in "applied" optimization, worthless.

Re: Applied Mathematical Programming (1977)

#24
post #19

"Applied ..."? On the Optimization Ph.D. qualifying exam, got a "High Pass" and the best score in the class. In optimization answered a question in the Kuhn-Tucker constraint qualifications and had the paper accepted quickly in Mathematical Programming . Taught linear programming in a well-known business school for 5 years. NYC had a few users, in a loose group, of linear programming but were not very good at it. Som…

> A fact of life: Nearly always people with money, power, and optimization problems don't understand optimization, fear and resent those who do, and choose just to avoid the subject. Worth considering this hard question: who optimized their life better? Them or us?

Yup. Applied Optimization 101: For decisions in life and career, avoid the applied math approach to optimization.

In particular, except maybe for some work in US national security, don't try to have the applied math of applied optimization for a career. Don't spend a lot of time studying optimization.

Later might be able to be the founder, COB, CEO of a startup where some math is a big advantage.

Re: Applied Mathematical Programming (1977)

#25
post #23

Earlier quoted context omitted.

You’re attributing these life challenges to your STEM PhD speciality? I am sensitive to your hardship, but optimization has nothing to do with it. People are succeeding with bachelor’s degrees in Latin.

The point of the post is that in my long experience there was not much about optimization that in any significant practical career sense was "applied", i.e., no jobs even to keep one from living on the streets, far from a career to buy a house and support a family. The point about my Ph.D. with a lot in optimization is that I was quite well qualified in the field, but even with all those qualifications "applied" was…

You should have tried wall street. At this point they are the real supporters of mathematicians. We had optimization problems everywhere and had physics PhDs reinventing mathematical algorithms and keeping things “proprietary”. Right now i work in a startup that essentially writes optimization routines for portfolio problems.

I will blame your phd advisor.

Re: Applied Mathematical Programming (1977)

#26
post #14
post #5

This is from 1977. I suppose it's ok for fundamentals but you can probably do better going with a modern text like Model Building in Mathematical Programming by H. Paul Williams (5th Edition)

H. P. Williams' book is often recommended for learning MIP formulations. However, I've found a cheat sheet to be much more helpful and practical for MIPs (not LPs but MIPs): https://msi-jp.com/xpress/learning/square/10-mipformref.pdf This contains the primitives you typically use in MIPs.

That PDF surprisingly does not define what "MIP" stands for, but I was able to deduce that it stands for Mixed Integer Programming.

Re: Applied Mathematical Programming (1977)

#27
post #19

"Applied ..."? On the Optimization Ph.D. qualifying exam, got a "High Pass" and the best score in the class. In optimization answered a question in the Kuhn-Tucker constraint qualifications and had the paper accepted quickly in Mathematical Programming . Taught linear programming in a well-known business school for 5 years. NYC had a few users, in a loose group, of linear programming but were not very good at it. Som…

> A fact of life: Nearly always people with money, power, and optimization problems don't understand optimization, fear and resent those who do, and choose just to avoid the subject.

Food for thought: the solution to real-world optimization problems is often dictated by constraints instead of optimal values.

This means that if you fail to understand the constraints, or even fail to identify them, then whatever your solution to the problem is, it will be wrong. And it will be obvious to those who are aware of the constraints.

Now, you're complaining that those presenting you with problems "don't understand optimization". From your anecdotes you were the one tasked with clarifying things to them. From the sound of it, you didn't accomplished that, and it was unclear to stakeholders whether your output even provided any value worth keeping.

Have you ever considered the possibility that you failed to understand the actual problems presented to you and even failed to clarify why your output was aligned with anyone's best interests?

Re: Applied Mathematical Programming (1977)

#28
post #23

Earlier quoted context omitted.

The point of the post is that in my long experience there was not much about optimization that in any significant practical career sense was "applied", i.e., no jobs even to keep one from living on the streets, far from a career to buy a house and support a family. The point about my Ph.D. with a lot in optimization is that I was quite well qualified in the field, but even with all those qualifications "applied" was…

You should have tried wall street. At this point they are the real supporters of mathematicians. We had optimization problems everywhere and had physics PhDs reinventing mathematical algorithms and keeping things “proprietary”. Right now i work in a startup that essentially writes optimization routines for portfolio problems. I will blame your phd advisor.

Wall Street?

I was in NY and close enough to NYC. I'd just published a paper in anomaly detection in complex systems, gave a talk at the main NASDAQ server farm, and later at Morgan Stanley. No real interest.

Sent a copy of my anomaly paper to a hedge fund, got an interview, was asked by one of their junior people "If know the correlation between A and B and that between B and C, what about A and C"? Okay, maybe: Start with the cosine of the sum of two angles???

Asked them for a reference on investing math -- did already know about the old Markowitz work, the efficient frontier, the role of quadratic optimization, about to do more with stochastic differential equations for the Black-Scholes work -- got the book they mentioned, saw that its math was all junk, and didn't follow up with the A and C contact, a mistake.

Did send a resume to Simons.

Looked into deterministic optimal control, Athens and Falb, talked with Athens, later talked with an Athens student who knew something about Simons and claimed that he hired mostly Russian mathematicians. So gave up. A mistake. I was naive.

Later, of course, Simons explained that he liked people who, say, via math but any math, had shown some ability, and I had some evidence I could have shown. My Math SAT was high enough that maybe I even beat Simons?

I was naive: Assumed that a carefully written resume was necessary and sufficient and that anything else was superfluous and unwelcome.

Nope: In practice in the real world, keep trying different things. Do send reprints of published papers. E.g., when I was at Georgetown, computer center staff and teaching computer science, a prof had some teaching software as a front end to the IBM SSP (scientific subroutine package) and in testing found that two of the IBM routines were too slow and the third had poor numerical accuracy. So, I wrote plug compatible versions -- used some (n)ln(n) software and some tricky double use of memory to replace the n^2 software and used some Forsythe and Moler work to fix the accuracy problem -- seemed too simple to me, but COULD have sent Simons that work. Once did get a lecture on differential geometry from a student of A. Gleason and had a copy of some S. Chern notes -- could have studied those and sent something to Simons. How'd I know Simons knew Chern??

There is a recent remark: "Don't give up. Keep plugging".

I was naive. Knew much more about math and computing than people and personality.

Since WWII, the US military has pushed hard to have more -- students, professors, and research -- in math and science. In high school, taught myself the math, learned the physics at a glance, otherwise goofed off (had a girlfriend drop dead gorgeous), but did well on the state standardized tests, so got sent to summer math/physics enrichment programs. I swallowed the bait hook, line, and sinker. I'd recommend:

"Always look for the hidden agenda."

"Believe none of what you hear, half of what you see, still that will be twice too much."

"Who you know can be more important than what you know."

There were some opportunities to "know" some powerful people, but I was naive.

My Ph.D. advisor was a nice guy, but I got to him after the fallout of a bad civil war in the faculty and never much talked with him. For my dissertation, some applied math, and had the main idea on an airplane flight before the Ph.D. program, in the first year wrote a 50 page first draft, later cleaned up the math, used Fubini's theorem in a short proof that my math was optimal, wrote some illustrative software, typed in the paper, showed it to my advisor and the rest of the department, had a famous guy a Chair of an orals committee to review the dissertation, and graduated. My advisor and one of the faculty (connected in DC and later President at an Ivy) knew a LOT about politics, but I was naive.

For a while, my career, in computing but with some math on the side, e.g., the FFT and digital filtering of Navy sonar signals, was going well, so I got the Ph.D. in applied math just to do better in THAT career and with ZERO intentions to be a professor or do academic research. That career direction was MY idea, mine alone, ..., a BAD situation!!! I was naive.

Re: Applied Mathematical Programming (1977)

#29
post #23

Earlier quoted context omitted.

You’re attributing these life challenges to your STEM PhD speciality? I am sensitive to your hardship, but optimization has nothing to do with it. People are succeeding with bachelor’s degrees in Latin.

The point of the post is that in my long experience there was not much about optimization that in any significant practical career sense was "applied", i.e., no jobs even to keep one from living on the streets, far from a career to buy a house and support a family. The point about my Ph.D. with a lot in optimization is that I was quite well qualified in the field, but even with all those qualifications "applied" was…

Sounds like you opened up the newspaper and scanned for “mathematician”. Leveraging phd research into a great job is a tough. Re-skilling into a normie engineer/technician/analyst, is not.

My point is not to criticize your job hunting skills, it’s to suggest that this an undue psychological burden in your life and is perhaps masking other causes and personal challenges.

Re: Applied Mathematical Programming (1977)

#30
post #24

Earlier quoted context omitted.

> A fact of life: Nearly always people with money, power, and optimization problems don't understand optimization, fear and resent those who do, and choose just to avoid the subject. Worth considering this hard question: who optimized their life better? Them or us?

Yup. Applied Optimization 101: For decisions in life and career, avoid the applied math approach to optimization. In particular, except maybe for some work in US national security, don't try to have the applied math of applied optimization for a career. Don't spend a lot of time studying optimization. Later might be able to be the founder, COB, CEO of a startup where some math is a big advantage.

> Yup. Applied Optimization 101: For decisions in life and career, avoid the applied math approach to optimization.

It pains me to say this, but might it ever crossed your mind that the problem does not lie in applied mathematics of even optimization?

We need soft skills to push the output from hard skills, and interacting with decision-makers requires people skills, not hard skills.

If you can't communicate with someone, it doesn't really matter what the numbers say. Do you get what I mean?

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