1. SUMMARY OF MY PROFESSIONAL
BACKGROUND AND RESEARCH OBJECTIVES
My Professional Background:
I have an M.S. and a Ph.D. in Civil Engineering
(Structures, with a Minor in Computer Science) from the
University of Connecticut, Storrs, CT (UCONN).
However, my Research interests in later years have shown
a distinct preference for Optimization, Operations
Research (OR), and Computational Techniques. I have
also done some work in the area of Artificial Intelligence
(AI), and have worked on a highly novel and promising
Approach using Supervised Learning to develop a
Forward Propagation Algorithm to accommodate even
fuzzy logic. This Algorithm would be inherently faster
than Rummelhart's Backpropagation Algorithm, since
adding new layers to the Neural Network becomes a
locally additive process, and would not involve retraining
the entire neural network to accommodate each such
addition, as in the Backpropagation Algorithm.

2. The foregoing diverse background has also given me the
confidence that I could successfully tackle new Research
challenges, as they would present themselves during
Collaborative Research. I would also welcome
opportunities to learn new skills. My other Research
Interests would relate to the use of Big Data (Hadoop,
MapReduce), Java (JavaScript), C++, Python, R, SQL,
and Cloud Computing.
My professional experience during my Research has
likewise been quite eclectic, in that I spent about 12 years
in the Corporate Environment, even did Contract
Engineering, and taught Math and English on a part-time
basis at the High School level while I was engaged in
sponsoring my Optimization Research, and finally settled
into the role of a Self-employed R&D Professional
competing for Federal Research Grants. I have also done
some Consulting for Engineering Companies like Stone &
Webster and the erstwhile Badger Co.
With my Structural Engineering Background, I also
acquired valuable Application Programming Experience
using Fortran, when I developed a comprehensive

3. Software System to analyze Structural Space Frames
using Matrix Structural Analysis, named by me
"MASTRAN", as also many other Software Applications
for Structural Design during my tenure as a Structural
Analyst at the erstwhile Badger Co. (later acquired by
Raytheon), and Nuclear Class I Piping Code Check for
Seismic Safety ("NCCODE" ) for Stone & Webster Engg.
Corp.
I have also acquired valuable Research experience in
investigating the safety of Thick-walled Reinforced
Concrete Structures, such as the Nuclear Reactor
Containments and LNG Tank Structures under Shock,
Impact, and Blast Loadings. Incidentally, Structural
Dynamics formed a significant part of my Doctoral
Dissertation, wherein I developed a new Analytical
Algorithm to compute good approximations to the First
Few Largest Eigenvalues of a Positive Definite Matrix
speedily.
I would be happy to furnish my detailed Resume', if
desired.

4. Earlier on, I did get Federal Research Grants from the
National Science Foundation (NSF) for Structural
Optimization, but in recent years, Federal Funding
Agencies have shown a distinct preference for funding
Universities and larger Outfits. That has been the
motivation behind my present Proposal for Collaborative
Research, following my earlier sustained research effort,
so as to improve the prospects of securing Federal
Research Funding, if such were to be warranted.
As a result of my efforts to secure Federal Research
Funding in the past, I have acquired considerable
experience in Proposal Preparation to compete for Federal
Research Grants, if need be, which would stand me in
good stead during my Research.
My Doctoral Advisory Committee was interdepartmental,
and comprised of:
a) Dr. H. Kardestuncer, Associate Professor, Civil Engr.
Dept. (Also, Editor-in-Chief, Finite Element Handbook),
Major Advisor.

5. b) Dr. Roman Solecki, Professor, Mech. Engr. Dept.,
Associate Advisor.
c) Prof. J.L.C. Lof, Director, Computer Center, Associate
Advisor.
None of the foregoing is currently on the UCONN
Faculty due to retirement. Incidentally, my GPA for the
Graduate Program was 3.9.
I am a naturalized U.S. Citizen.
Technical Excellence of Proposed Research:
The Research proposed herewith would result in
sensational new Techniques in Mathematical
Programming (Constrained Optimization) for Solving
Large Linear Programming (LP) and Nonlinear
Programming (NLP) Problems.
The proposed new LP Algorithm would be the first ever

6. "Strongly Polynomial" Algorithm, which would be an
order of magnitude faster than the existing methods for
solving large LP problems. It would also overcome all of
the drawbacks of the existing methods, viz., The Simplex
Algorithm of Dantzig and the Interior Point Method by
Karmarkar (or its Variants), which are iterative in nature.
The new Algorithm would be a Direct Method completing
the solution in a fixed number of steps, a number that is a
simple function of the number of constraints and the
number of variables. The Complexity Bound for
Karmarkar Method, on the other hand, which is a "weakly
polynomial" method, includes a factor which represents
the Problem Size. Thus, it would be seen to be an order of
magnitude slower than the new Algorithm, to be termed
the "LILP Algorithm", for solving large LP problems. The
new Algorithm uses a highly original approach in
extending concepts from Polyhedral Theory.
Further, both the Simplex and the Interior Point Methods
are sensitive to the structure and density of the Constraint
Coefficient Matrix. Incidentally, the Simplex Algorithm,
which performs generally well, has an Exponential
Complexity Bound which is actually attained for certain
problems. The LILP Algorithm, however, would be

7. completely insensitive to the structure and density of the
constraint coefficient matrix, although it would afford
scope for the utilization of sparsity inherent in the
constraint coefficient matrix. Thus, the new LILP
Algorithm would be a very robust solution method for
large LP Problems.
The new NLP Algorithm would also be very fast and
robust for solving large NLP Problems. Moreover, it
would permit the use of coarse step changes during the
solution, and would be constructive in a sense, as it would
enable monitoring of the approach to the optimum
solution by checking the Optimality Criterion (Kuhn-
Tucker Conditions). It would operate in the constraint
function space, treating, in effect, constraints as variables,
and would also apply the Variational Concepts in the
solution. It would thus be a highly efficient, novel
addition to the NLP solution methods.
The foregoing Research would also result in a Research
Monograph in two Volumes, to be titled:
"New Frontiers in Computational Econometrics,

8. Mathematical Programming, and Optimization"
Volume I would present the LP Research and the details
of the new LILP Algorithm for solving large LP
Problems. Formal Analytical Propositions would be
developed with proofs, and there would be a complete
Chapter dedicated to several fully solved Problems, using
the new LILP Algorithm.
Volume II would present the NLP research and the details
of the new Variational NLP Algorithm.
Incidentally, the importance and value of the Proposed
Research cannot be overstressed, because Optimization in
various forms has a ubiquitous role in Science and
Engineering, and, notably, in Applied Sciences like
Operations Research (OR).
The proposed Research would also have an important
commercial component. It may be recalled that the
Interior Point Method to solve LP Problems developed by

9. Dr. Narendra Karmarkar in 1984 was fully
commercialized by his then Employer, A.T.& T. Bell
Labs. The Proposed Research could likewise be
commercialized by the Sponsor of this Research.
The Proposed Research would represent the overall
Research Effort to be undertaken by the Author as a
Resident Scientist/Analyst working for the Sponsor.
The sponsoring Organization would stand to benefit from
the Proposed Research in three ways: a) The novelty and
importance of the Research would represent a major
breakthrough in Basic Research in Mathematical
Programming (Constrained Optimization); b) The
uniqueness and excellence of the Research would greatly
enhance the prestige and reputation of the Sponsoring
Organization; and, c) The resulting Algorithms can be
profitably commercialized by the Sponsoring
Organization.
I would also be happy to participate in new Research
Topics introduced by the Sponsor or recommended by me
at the suggestion of my Sponsor.

10. In closing, I believe that my Proposal for Collaborative
Research would be a resounding success, and I am
therefore highly optimistic that I would be able to give a
very satisfactory account of myself, given the opportunity
to embark upon such a Collaborative Effort, and would
bring credit and acclaim to my Sponsor.
Sd./ Dr. Suresh R. Phansalkar.
My Email: sureshphn@yahoo.com