This document summarizes Pratik Poddar's 2008 summer internship at The Chinese University of Hong Kong. The internship focused on three topics: 1) the polyblock algorithm for monotonic optimization, 2) network utility maximization, and 3) Internet congestion control problems. For each topic, the document provides background information and discusses recent related work, focusing on challenges with non-convex optimization problems and how algorithms like the polyblock algorithm and admission control can help address these challenges.
PDF version of slides explains the various optimization algorithms used in deep learning and a comparison between them. It also has a brief about the ICML papers "Descending through a Crowded Valley — Benchmarking Deep Learning Optimizers" and "Optimizer Benchmarking Needs to Account for Hyperparameter Tuning."
If you have any queries, you can reach out to me at @RakshithSathish on Twitter or rakshith-sathish on LinkedIn.
In statistics, machine learning, and information theory, dimensionality reduction or dimension reduction is the process of reducing the number of random variables under consideration by obtaining a set of principal variables.
Unsupervised Anomaly Detection with Isolation Forest - Elena SharovaPyData
PyData London 2018
This talk will focus on the importance of correctly defining an anomaly when conducting anomaly detection using unsupervised machine learning. It will include a review of Isolation Forest algorithm (Liu et al. 2008), and a demonstration of how this algorithm can be applied to transaction monitoring, specifically to detect money laundering.
---
www.pydata.org
PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.
PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
PDF version of slides explains the various optimization algorithms used in deep learning and a comparison between them. It also has a brief about the ICML papers "Descending through a Crowded Valley — Benchmarking Deep Learning Optimizers" and "Optimizer Benchmarking Needs to Account for Hyperparameter Tuning."
If you have any queries, you can reach out to me at @RakshithSathish on Twitter or rakshith-sathish on LinkedIn.
In statistics, machine learning, and information theory, dimensionality reduction or dimension reduction is the process of reducing the number of random variables under consideration by obtaining a set of principal variables.
Unsupervised Anomaly Detection with Isolation Forest - Elena SharovaPyData
PyData London 2018
This talk will focus on the importance of correctly defining an anomaly when conducting anomaly detection using unsupervised machine learning. It will include a review of Isolation Forest algorithm (Liu et al. 2008), and a demonstration of how this algorithm can be applied to transaction monitoring, specifically to detect money laundering.
---
www.pydata.org
PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.
PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Slides for a talk about Graph Neural Networks architectures, overview taken from very good paper by Zonghan Wu et al. (https://arxiv.org/pdf/1901.00596.pdf)
Non concave network utility maximization - A distributed optimization approachWasif Hafeez
This paper proposes an algorithm for optimal decentralized traffic engineering in communication networks. We aim at distributing the traffic among the available routes such that the network utility is maximized. In some practical applications, modeling network utility using non-concave functions is of particular interest, e.g., video streaming.
Slides for a talk about Graph Neural Networks architectures, overview taken from very good paper by Zonghan Wu et al. (https://arxiv.org/pdf/1901.00596.pdf)
Non concave network utility maximization - A distributed optimization approachWasif Hafeez
This paper proposes an algorithm for optimal decentralized traffic engineering in communication networks. We aim at distributing the traffic among the available routes such that the network utility is maximized. In some practical applications, modeling network utility using non-concave functions is of particular interest, e.g., video streaming.
Parallel Patterns for Window-based Stateful Operators on Data Streams: an Alg...Tiziano De Matteis
Talk given at HLPP 2015
For the version with transition please check: https://docs.google.com/presentation/d/1yhsSff97f434wR-VA1szlqKxx52YMYKkdw1GVkBDyF8/edit?usp=sharing
In this deck from the HPC User Forum in Tucson, Steve Reinhardt from D-Wave Systems presents: Quantum Computing - Timing is Everything.
"Despite the incredible power of today’s supercomputers, there are many complex computing problems that can’t be addressed by conventional systems. Our need to better understand everything, from the universe to our own DNA, leads us to seek new approaches to answer the most difficult questions. While we are only at the beginning of this journey, quantum computing has the potential to help solve some of the most complex technical, scientific, national defense, and commercial problems that organizations face. We expect that quantum computing will lead to breakthroughs in science, engineering, modeling and simulation, healthcare, financial analysis, optimization, logistics, and national defense applications."
Watch the video: https://wp.me/p3RLHQ-ir5
Learn more: https://www.dwavesys.com/
and
http://hpcuserforum.com
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Slides of the first Mulesoft Cosenza Meetup titled "Anypoint Platform for modern web apis development & Implementing a Retry Logic with RabbitMQ and the Amqp connector".
Basic Talk. 90 minute talk to an audience of Freshmen and Sophomores of IIT Bombay on 23/02/10 as a part of Science Week. Organised by Web and Coding Club. Place: GG 101 (Elec Department)
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
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End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
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- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
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Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
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Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
Non-convex Optimization in Networks
1. Summer 2008 Internship
Report
Advisor: Prof. Angela Y. Zhang
The Chinese University of Hong Kong, Hong Kong
Student: Pratik Poddar
Indian Institute of Technology Bombay, India
Topic: NonConvex Optimization
Problems in Networks
3. Basics of Optimization
●
Standard Optimization problem
●
Linear Optimization problem
●
Convex Optimization problem
●
Monotonic Optimization problem
4. Polyblock Algorithm
●
We have had two major events in the history of
optimization theory.
●
The first was linear programming and simplex
method in late 1940s early 1950s.
●
The second was convex optimization and interior
point method in late 1980s early 1990s.
5. Polyblock Algorithm
●
Convex optimization problems are known to be
solved, very reliably and efficiently.
●
"..in fact, the great watershed in optimization isn't
between linearity and nonlinearity, but convexity
and nonconvexity" R. Tyrrell Rockafellar, in
SIAM Review, 1993
6. Polyblock Algorithm
●
Current research in optimization is mainly to have
that third event Solving nonconvex optimization
efficiently. Although solving convex optimization
problems is easy and nonconvex optimization
problems is hard, but a variety of approaches have
been proposed to solve nonconvex optimization
problems.
7. Polyblock Algorithm
●
In 2000, H. Tuy proposed an algorithm to solve
optimization problems involving d.i functions under
monotonic constraints.
●
This algorithm (Polyblock Algorithm) was inspired
by the idea of Polyhedral Outer Approximation
Method for maximizing a quasiconvex function
over a convex set.
8. Polyblock Algorithm
●
What is a polyblock?
●
Then what is the difference between a polyblock and
a polyhedron?
●
What are its properties?
●
How is polyblock algorithm implemented?
14. Network Utility
Maximization
●
The framework of Network Utility Maximization
(NUM) has found many applications in network rate
allocation algorithms and Internet Congestion
Control Protocols.
15. Network Utility
Maximization
●
Problem: Consider a network with L links, each with
a fixed capacity cl bps, and S sources (i.e. end
users), each transmitting at the rate of xs bps. Each
source s uses the set L(s) of links in its path and has
a utility function Us(xs). Each link l is shared by a set
S(l) of sources. So, Network Utility Maximization is
basically the problem of maximizing the total utility
of the system over source rates subject to congestion
constraints for all links.
17. Network Utility
Maximization
●
Concave Utilities Follows from Law of
Diminishing Marginal Utilities. Convex
Optimization Problem.
●
U(x) = log (1+x)
U(x)
x
18. NUM for Concave Utilities
●
The problem of Network Utility Maximization in
case of concave utilities is essentially a convex
optimization problem which is solvable efficiently
and exactly.
19. Network Utility
Maximization
●
NonConcave Utilities – In multimedia applications
on Internet, the utilities are nonconcave. Non
convex optimization problem.
●
U(x) = (1 + eax+b) 1
U(x)
x
20. NUM for Non-Concave
Utilities
●
The problem is a nonconvex optimization problem.
Three ways have been suggested to solve it.
●
In [3], a 'selfregulation' heuristic is proposed,
however it converges only to a suboptimal solution.
●
In [4], a set of sufficient and necessary conditions is
presented under which the canonical distributed
algorithm converges to a global optimal solution.
However, these conditions may not hold in most
cases.
21. NUM for Non-Concave
Utilities
●
In [2], Using a family of convex SDP relaxations
based on the sumofsquares method and
Positivestellensatz Theorem in real algebraic
geometry, a centralized computational method to
bound the total network utility in polynomial time is
proposed.
●
This is effectively a centralized method to compute
the global optimum when the utilities can be
transformed into polynomial utilities.
22. NUM for Non-Concave
Utilities
●
In summary, currently there is no theoretically
polynomialtime algorithm (distributed or
centralised) known for nonconcave utility
maximization.
●
We worked to find ways to convexify the above
problem.
23. Idea and motivation
●
The set may not be a convex set but if it can be
broken into a constant number of convex sets, we
can solve the problem in polynomial time.
27. Motivation
●
By this method, we can solve NUM problem in
polynomial time. NUM finds applications in
network rate allocation algorithms and Internet
Congestion Control Protocol.
29. Internet Congestion Control
●
Internet relies on congestion control implemented in
the endsystems to prevent offered load exceeding
network capacity, as well as allocate network
resources to different users and applications.
●
In the past, the applications (email, file transfer) had
concave utilities (i.e were elastic). As number of
multimedia applications are increasing, there are
various talks on different congestion controls.
30. Internet Congestion Control
●
In [5], It has been argued that fairness congestion
control does not maximize the network's utility.
Infact, Admission control is shown to be better
control (in terms of both elastic and inelastic
utilities) than Fair Congestion Control in a
simplified case.
●
Let α be the desired rate of inelastic flows, m be the
number of inelastic flows and n be the number of
elastic flows.
31. Fair Congestion Control
●
Perform TCPfriendly congestion control. We model
it as the same fair congestion control as adopted for
elastic flows, with a slight difference. When the fair
share is smaller than α, then the fair share is used,
but when the fair share is greater than α, the
inelastic flow would still consume α.
32. Admission Control
●
Perform admission control but no congestion control
once admitted. Assume the network already has n
elastic flows and m inelastic flows, a new inelastic
flow is admitted iff nε + (m1)α <=1
●
Here ε represents the minimum rate admission
control scheme tries to leave for elastic traffic.
Depending upon α, we can have two cases:
33. Aggressive Admission
Control
●
ε <<< α – The arriving flow is admitted as long as it
is possible to allocate to it the desired rate of α, even
if this means all elastic flows have to run at their
minimum rate of ε.
●
So, an inelastic flow is admitted iff (m+1)α ≤ 1 and
an elastic flow is always admitted.
34. Fair Admission Control
●
ε = α – The arriving flow is admitted as long as its
desired rate is no greater than the prevailing fair
share for each elastic flow.
●
So, an inelastic flow is admitted iff (m+n+1)α ≤ 1
and an elastic flow is always admitted.
●
In [5], it is proved that Fair Admission control is
better than both Aggressive Admission contol and
Fair Congestion Control.
35. Idea
●
Solving the optimization problem using the
polyblock algorithm would help us to prove (or
disprove) that admission control is better than fair
congestion control.
●
Status: Coding to check it under progress.
37. Bibliography
●
[1] H. Tuy, ”Monotonic Optimization: Problems and Solution Approaches”,
SIAM Journal on Optimization, 11:2(2000), 464494
●
[2] M. Fazel, M. Chiang, ”Network Utility Maximization With Nonconcave
Utilities Using SumofSquares Method”, Proc. IEEE CDC, December 2005
●
[3] J.W.Lee, R.R. Mazumdar, N. Shroff, ”Nonconvex optimization and rate
control for multiclass services in the Internet”, Proc. IEEE Infocom, March
2004
●
[4] M. Chiang, S. Zhang, P. Hande, ”Distributed rate allocation for inelastic
flows: Optimization framework, optimality conditions, and optimal
algorithms”, Proc. IEEE Infocom, March 2005
●
[5] D. M. Chiu, A. S. W. Tam, ”Fairness of traffic controls for inelastic
flows in the Internet”, Comput. Netw. (2007), doi:10.1016/j.comnet.
2006.12.2006