OPTEX - MATHEMATICAL MODELING AND MANAGEMENT SYSTEM - is a META-FRAMEWORK for Mathematical Programming.
Oriented towards the design, implementation and setup of decision support systems based in mathematical programming with special emphasis in the development of final user apps:
- The algebraic formulation is independent from any programming language
- The models can be connected with any data server
Thereby generating apps using multiple commercial or noncommercial tech according to clients’ needs
OPTEX MATHEMATICAL MODELING AND MANAGEMENT SYSTEMJesus Velasquez
OPTEX MATHEMATICAL MODELING AND MANAGEMENT SYSTEM
is a META-FRAMEWORK for Mathematical Programming.
Oriented towards the design, implementation and setup of decision support systems based in mathematical programming with special emphasis in the development of final user apps:
- The algebraic formulation is independent from any programming language
- The models can be connected with any data server
Thereby generating apps using multiple commercial or noncommercial tech according to clients’ needs
A general introduction to GPGPU and an application involving solving large preconditioning problems with Domain Decomposition. Code is available at http://sourceforge.net/projects/cudasolver/ .
MediaEval 2016 - UPMC at MediaEval2016 Retrieving Diverse Social Images Taskmultimediaeval
Presenter: Sabrina Tollari
UPMC at MediaEval 2016 Retrieving Diverse Social Images Task In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Sabrina Tollari
Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_14.pdf
Video: https://youtu.be/_uletoYIZGQ
Abstract: In the MediaEval 2016 Retrieving Diverse Social Images Task, we proposed a general framework based on agglomerative hierarchical clustering (AHC). We tested the provided credibility descriptors as a vector input for our AHC. The results on devset showed that this vector based on the credibility descriptors is the best feature, but unfortunately that is not confirmed on testset. To merge several features, we chose to merge feature similarities. Tests on devset showed that to merge similarities using linear or weighted-max operators gave, most of the time, better results than using only one feature. This results is partially confirmed on testset.
OPTEX MATHEMATICAL MODELING AND MANAGEMENT SYSTEMJesus Velasquez
OPTEX MATHEMATICAL MODELING AND MANAGEMENT SYSTEM
is a META-FRAMEWORK for Mathematical Programming.
Oriented towards the design, implementation and setup of decision support systems based in mathematical programming with special emphasis in the development of final user apps:
- The algebraic formulation is independent from any programming language
- The models can be connected with any data server
Thereby generating apps using multiple commercial or noncommercial tech according to clients’ needs
A general introduction to GPGPU and an application involving solving large preconditioning problems with Domain Decomposition. Code is available at http://sourceforge.net/projects/cudasolver/ .
MediaEval 2016 - UPMC at MediaEval2016 Retrieving Diverse Social Images Taskmultimediaeval
Presenter: Sabrina Tollari
UPMC at MediaEval 2016 Retrieving Diverse Social Images Task In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Sabrina Tollari
Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_14.pdf
Video: https://youtu.be/_uletoYIZGQ
Abstract: In the MediaEval 2016 Retrieving Diverse Social Images Task, we proposed a general framework based on agglomerative hierarchical clustering (AHC). We tested the provided credibility descriptors as a vector input for our AHC. The results on devset showed that this vector based on the credibility descriptors is the best feature, but unfortunately that is not confirmed on testset. To merge several features, we chose to merge feature similarities. Tests on devset showed that to merge similarities using linear or weighted-max operators gave, most of the time, better results than using only one feature. This results is partially confirmed on testset.
ADMM-Based Scalable Machine Learning on Apache Spark with Sauptik Dhar and Mo...Databricks
Apache Spark is rapidly becoming the de facto framework for big-data analytics. Spark’s built-in, large-scale Machine Learning Library (MLlib) uses traditional stochastic gradient descent (SGD) to solve standard ML algorithms. However, MlLib currently provides limited coverage of ML algorithms. Further, the convergence of the adopted SGD approach is heavily dictated by issues such as step-size selection, conditioning of the problem and so on, making it difficult for adoption by non-expert end users.
In this session, the speakers introduce a large-scale ML tool built on the Alternating Direction Method of Multipliers (ADMM) on Spark to solve a gamut of ML algorithms. The proposed approach decomposes most ML problems into smaller sub-problems suitable for distributed computation in Spark.
Learn how this toolkit provides a wider range of ML algorithms, better accuracy compared to MLlib, robust convergence criteria and a simple python API suitable for data scientists – making it easy for end users to develop advanced ML algorithms at scale, without worrying about the underlying intricacies of the optimization solver. It’s a useful arsenal for data scientists’ ML ecosystem on Spark.
Talk given at Los Alamos National Labs in Fall 2015.
As research becomes more data-intensive and platforms become more heterogeneous, we need to shift focus from performance to productivity.
Applying Linear Optimization Using GLPKJeremy Chen
A brief introduction to linear optimization with a focus on applying it with the high-quality open-source solver GLPK.
Originally prepared for an intra-department sharing session.
Dewesoft is designing and manufacturing versatile and easy-to-use data acquisition systems. The products are the ultimate tools for every test and measurement engineer. The presentation includes a general catalog of Dewesoft products and applications.
Achitecture Aware Algorithms and Software for Peta and Exascaleinside-BigData.com
Jack Dongarra from the University of Tennessee presented these slides at Ken Kennedy Institute of Information Technology on Feb 13, 2014.
Listen to the podcast review of this talk: http://insidehpc.com/2014/02/13/week-hpc-jack-dongarra-talks-algorithms-exascale/
What is high performance Computing, examples of OpenMP, MPI and Pthreads. How obtain HPC benefits using OpenSolaris.
This version was presented at OpenSolaris Day in Porto Alegre, Brazil, in April 16, 2008.
C++ and Assembly: Debugging and Reverse Engineeringcorehard_by
C++ and Assembly: Debugging and Reverse Engineering, Mike Gelfand
Мы привыкли рассматривать язык ассемблера как низкоуровневый. Пишем мы на нём сравнительно редко: для описания логики, невыразимой или трудновыразимой языками типа С++, или же для реализации критических ветвей исполнения, для которых компиляторы генерируют недостаточно оптимальный код. В своём докладе я расскажу о пользе базовых умений чтения и анализа ассемблерного кода и ситуациях, в которых желание время от времени опускаться на этот нижний уровень помогает решать проблемы и делать мир лучше.
Learning to Compose Domain-Specific Transformations for Data AugmentationTatsuya Shirakawa
A. J. Ratner, H. R. Ehrenberg, et al., “Learning to Compose Domain-Specific Transformations for Data Augmentation”, NIPS2017
(https://papers.nips.cc/paper/6916-learning-to-compose-domain-specific-transformations-for-data-augmentation)
Slides for NIPS2017 paper reading meet-up @Tokyo
https://abeja-innovation-meetup.connpass.com/event/75189/
Optimization of Continuous Queries in Federated Database and Stream Processin...Zbigniew Jerzak
The constantly increasing number of connected devices and sensors results in increasing volume and velocity of sensor-based streaming data. Traditional approaches for processing high velocity sensor data rely on stream processing engines. However, the increasing complexity of continuous queries executed on top of high velocity data has resulted in growing demand for federated systems composed of data stream processing engines and database engines. One of major challenges for such systems is to devise the optimal query execution plan to maximize the throughput of continuous queries.
In this paper we present a general framework for federated database and stream processing systems, and introduce the design and implementation of a cost-based optimizer for optimizing relational continuous queries in such systems. Our optimizer uses characteristics of continuous queries and source data streams to devise an optimal placement for each operator of a continuous query. This fine level of optimization, combined with the estimation of the feasibility of query plans, allows our optimizer to devise query plans which result in 8 times higher throughput as compared to the baseline approach which uses only stream processing engines. Moreover, our experimental results showed that even for simple queries, a hybrid execution plan can result in 4 times and 1.6 times higher throughput than a pure stream processing engine plan and a pure database engine plan, respectively.
While compute becomes faster and cheaper we are tempted to abandon sanity and shield ourselves from reality and laws of physics. The resulting mess of monstrous Slack instances rampaging across our RAM should makes us stop (because our computers did it already) and wonder where did we go wrong? Rising developer salaries and time to market pace are tempting us to abandon all hope for optimising our code and understanding our systems.
Contrary to what casual reader could think this is a deeply technical presentation. We will gaze into hardware counters, NUMA nodes, vector registers and that darkness will stare back at us.
All this to get a taste of what is possible on current hardware, to learn the COST of scalability and forever change how you feel when accessing invoice list in your local utilities provider UI so that after 20s of waiting all 12 elements will be displayed (surely Cthulhu must be eating their compute because it is NOT possible Tauron hosts it’s billing services on FIRST GEN IPHONE).
Tips And Tricks For Bioinformatics Software Engineeringjtdudley
This is a talk I've given twice at Stanford recently. It's essentially a brain dump of my thoughts on being a Bioinformatician with lots of links to useful tools.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
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
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
ADMM-Based Scalable Machine Learning on Apache Spark with Sauptik Dhar and Mo...Databricks
Apache Spark is rapidly becoming the de facto framework for big-data analytics. Spark’s built-in, large-scale Machine Learning Library (MLlib) uses traditional stochastic gradient descent (SGD) to solve standard ML algorithms. However, MlLib currently provides limited coverage of ML algorithms. Further, the convergence of the adopted SGD approach is heavily dictated by issues such as step-size selection, conditioning of the problem and so on, making it difficult for adoption by non-expert end users.
In this session, the speakers introduce a large-scale ML tool built on the Alternating Direction Method of Multipliers (ADMM) on Spark to solve a gamut of ML algorithms. The proposed approach decomposes most ML problems into smaller sub-problems suitable for distributed computation in Spark.
Learn how this toolkit provides a wider range of ML algorithms, better accuracy compared to MLlib, robust convergence criteria and a simple python API suitable for data scientists – making it easy for end users to develop advanced ML algorithms at scale, without worrying about the underlying intricacies of the optimization solver. It’s a useful arsenal for data scientists’ ML ecosystem on Spark.
Talk given at Los Alamos National Labs in Fall 2015.
As research becomes more data-intensive and platforms become more heterogeneous, we need to shift focus from performance to productivity.
Applying Linear Optimization Using GLPKJeremy Chen
A brief introduction to linear optimization with a focus on applying it with the high-quality open-source solver GLPK.
Originally prepared for an intra-department sharing session.
Dewesoft is designing and manufacturing versatile and easy-to-use data acquisition systems. The products are the ultimate tools for every test and measurement engineer. The presentation includes a general catalog of Dewesoft products and applications.
Achitecture Aware Algorithms and Software for Peta and Exascaleinside-BigData.com
Jack Dongarra from the University of Tennessee presented these slides at Ken Kennedy Institute of Information Technology on Feb 13, 2014.
Listen to the podcast review of this talk: http://insidehpc.com/2014/02/13/week-hpc-jack-dongarra-talks-algorithms-exascale/
What is high performance Computing, examples of OpenMP, MPI and Pthreads. How obtain HPC benefits using OpenSolaris.
This version was presented at OpenSolaris Day in Porto Alegre, Brazil, in April 16, 2008.
C++ and Assembly: Debugging and Reverse Engineeringcorehard_by
C++ and Assembly: Debugging and Reverse Engineering, Mike Gelfand
Мы привыкли рассматривать язык ассемблера как низкоуровневый. Пишем мы на нём сравнительно редко: для описания логики, невыразимой или трудновыразимой языками типа С++, или же для реализации критических ветвей исполнения, для которых компиляторы генерируют недостаточно оптимальный код. В своём докладе я расскажу о пользе базовых умений чтения и анализа ассемблерного кода и ситуациях, в которых желание время от времени опускаться на этот нижний уровень помогает решать проблемы и делать мир лучше.
Learning to Compose Domain-Specific Transformations for Data AugmentationTatsuya Shirakawa
A. J. Ratner, H. R. Ehrenberg, et al., “Learning to Compose Domain-Specific Transformations for Data Augmentation”, NIPS2017
(https://papers.nips.cc/paper/6916-learning-to-compose-domain-specific-transformations-for-data-augmentation)
Slides for NIPS2017 paper reading meet-up @Tokyo
https://abeja-innovation-meetup.connpass.com/event/75189/
Optimization of Continuous Queries in Federated Database and Stream Processin...Zbigniew Jerzak
The constantly increasing number of connected devices and sensors results in increasing volume and velocity of sensor-based streaming data. Traditional approaches for processing high velocity sensor data rely on stream processing engines. However, the increasing complexity of continuous queries executed on top of high velocity data has resulted in growing demand for federated systems composed of data stream processing engines and database engines. One of major challenges for such systems is to devise the optimal query execution plan to maximize the throughput of continuous queries.
In this paper we present a general framework for federated database and stream processing systems, and introduce the design and implementation of a cost-based optimizer for optimizing relational continuous queries in such systems. Our optimizer uses characteristics of continuous queries and source data streams to devise an optimal placement for each operator of a continuous query. This fine level of optimization, combined with the estimation of the feasibility of query plans, allows our optimizer to devise query plans which result in 8 times higher throughput as compared to the baseline approach which uses only stream processing engines. Moreover, our experimental results showed that even for simple queries, a hybrid execution plan can result in 4 times and 1.6 times higher throughput than a pure stream processing engine plan and a pure database engine plan, respectively.
While compute becomes faster and cheaper we are tempted to abandon sanity and shield ourselves from reality and laws of physics. The resulting mess of monstrous Slack instances rampaging across our RAM should makes us stop (because our computers did it already) and wonder where did we go wrong? Rising developer salaries and time to market pace are tempting us to abandon all hope for optimising our code and understanding our systems.
Contrary to what casual reader could think this is a deeply technical presentation. We will gaze into hardware counters, NUMA nodes, vector registers and that darkness will stare back at us.
All this to get a taste of what is possible on current hardware, to learn the COST of scalability and forever change how you feel when accessing invoice list in your local utilities provider UI so that after 20s of waiting all 12 elements will be displayed (surely Cthulhu must be eating their compute because it is NOT possible Tauron hosts it’s billing services on FIRST GEN IPHONE).
Tips And Tricks For Bioinformatics Software Engineeringjtdudley
This is a talk I've given twice at Stanford recently. It's essentially a brain dump of my thoughts on being a Bioinformatician with lots of links to useful tools.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
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
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
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
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
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.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
2. OUR MISSION:
BRING THE BENEFITS OF OPTIMIZATION TECHNOLOGY
TO SOCIETY:
ENABLING PEOPLE TO APPLY OPTIMIZATION TECHNOLOGY
SUCCESSFULLY INTO THEIR ORGANIZATIONS
BEING ENTREPRENEURS OF NEW COMPANIES
THAT BRING THE BENEFITS OF OPTIMIZATION TECHNOLOGY TO SOCIETY
3. Why do you choose to
programming in any
specific optimization
technology when you can
programming in all tools
at the same time with
only one effort ?FICO™
XPRESS-MOSEL
CPLEX-OPL-ODM
IMPRESS
4. Why do you choose to
programming in any
specific optimization
technology when you can
programming in all tools
at the same time with
only one effort ?
The best way is to have
the mathematical models
in a meta-platform and in
a second phase go to any
specific commercial
platform.
5. As a part of its process of
technological innovation,
DW has developed an
optimization technology
called
OPTEX
Mathematical Modeling
Management System
which is oriented to
designing, implementing
and setting up large scale
optimization models for
the real word .
6. OPTEX IS A META-FRAMEWORK
ORIENTED TOWARDS THE DESIGN, IMPLEMENTATION AND SETUP OF DECISION
SUPPORT SYSTEMS BASED IN MATHEMATICAL PROGRAMMING WITH SPECIAL
EMPHASIS IN THE DEVELOPMENT OF FINAL USER APPS:
ALGEBRAIC FORMULATION IS INDEPENDENT FROM ANY PROGRAMMING
LANGUAGE
CAN BE CONNECTED WITH ANY DATA SERVER
THEREBY GENERATING APPS USING MULTIPLE COMMERCIAL OR NONCOMMERCIAL
TECH ACCORDING TO CLIENTS’ NEEDS
7. OPTEX Mathematical Modeling System,
was developed to support
DecisionWare’s mathematical modeling
projects since 1991.
15. A DECISION SUPPORT SYSTEM
IS AS A DECISION MAKING CHAIN
INTEGRATED BY A COLLECTION
OF MODELS AND DATA FLOW
16. PTA
Industrial Operations
Tactical Planning
DEM
Long/Medium/Short
Demand Planning
INV
Inventory
Policy
Medium / Short Term
Demand Projections
Inventory
Policy
Production
Goals
POD
Production
Schedule
DIS
Distribution
Schedule
Distribution
Goals
PCO
Sourcing
Sourcing
Goals
Production
Orders
Distribution
Orders
Sourcing
Orders
PES
Supply Chain Design
Short / Medium Term
Market Scenarios
Expansion
Plans
DSS
Short / Medium Term
Market Scenarios
21. ALGEBRAIC LANGUAGES
• Algebraic Programming Language
• Database Algebraic Language
USER INTERFACE
• Based in database tables
• Operates in LANs and WANs (“Cloud Computing”)
• Visual Interface (MS-Windows)
• Filling the blanks parameterization
SERVICES
• Data-Model Generator
• Final User Interface Generator
• General Language Model Generator (C, Java …), includes Matrix Generator
• Algebraic Language Model Generator (GAMS, IBM ILOG OPL, MOSEL , AIMMS … )
PROBLEM SOLUTION
• Basic problems: LP, MIP, QP, MIQP, NLP
• Large Scale Theory: Benders Partition, Lagrangean Relaxation, Disjunctive Programming, …
• Links to multiple optimization libraries (GUROBI, IBM CPLEX, XPREXX, COIN-MP, … )
• Automatic Generation of Non-anticipative Multistage Stochastic Programming (MSP)
• Parallel solution in computers grids
CONNECTIVITY
• ERP/WMS/TMS/AMS: Enterprise Information Systems
• GIS: Geographic Information Systems
• ASP: Applications Service Provider (MS-Project, Google MAPS, …)
ELEMENTS
23. ALGEBRAIC LANGUAGES OBJECTS
MATHEMATICAL DEFINITIONS
• Index, Sets, Parameters, Variables, Equations,
Objective Functions, Planning Horizons, Decision
Trees
DECISION SUPPORT SYSTEMS
• Problems = (Equations, Variables, Objective
Functions)
• Model = (Problems, Data Flows)
• DSS = (Models, Data Flows)
DATA MODEL
• DSN, Data Tables, Fields, Shell Windows, Data
Windows, Menus
25. OPTEX- DATABASE ALGEBRAIC LANGUAGE
SQL
Server
Internet - Intranet
MM
Server
MATHEMATICAL
MODEL
SERVER
INFORMATION
SYSTEM
EASY DEVELOPMENT MATHEMATICAL MODELS
IN A LAN-WAN ENVIRONMENT USING THE POWER
OF THE DATABASE SERVERS
26. OPTEX- DATABASE ALGEBRAIC LANGUAGE
SQL
Server
Internet - Intranet
MM
Server
MATHEMATICAL
MODEL
SERVER
INFORMATION
SYSTEM
THE IMPLEMENTATION OF A
DECISION SUPPORT SYSTEMS IS BASED IN
A FILLING THE BLANKS PROCESS
34. TIPO DE SERIE INTERPRETACIÓN
E
ESCALÓN
()
I
IMPULSO
(PULSE)
P
POLI LÍNEA
(POLY LINE)
OPTEX- DATABASE ALGEBRAIC LANGUAGE
MULTIPLES FORMS OF
DATA INTERPRETATION
35. JVB-08/94OPTEX
Min t j h CTt(GTjth)
sujeto a:
GDzth = uTN(z) LDuzth
GDzth + GHAzth + DEFzth = DEMzth
ENuth - jL1(u) GTEjuth
- vL2(u) LLvuth = 0
. . . .
z NOD
t = 1,T
h = 1,NH
z NOD
t = 1,T
h = 1,NH
u LIN
t = 1,T
h = 1,NH
VARIABLES
OPTEX- DATABASE ALGEBRAIC LANGUAGE
36. JVB-08/94OPTEX
Min t j h CTt(GTjth)
sujeto a:
GDzth = uTN(z) LDuzth
GDzth + GHAzth + DEFzth = DEMzth
ENuth - jL1(u) GTEjuth
- vL2(u) LLvuth = 0
. . . .
z NOD
t = 1,T
h = 1,NH
z NOD
t = 1,T
h = 1,NH
u LIN
t = 1,T
h = 1,NH
CONSTRAINTS
OPTEX- DATABASE ALGEBRAIC LANGUAGE
39. MO
IL
MO
WO MO
Tiempo
OPTEX- DATABASE ALGEBRAIC LANGUAGE
FOR DISCRETE TIME MOODELS, THE
PLANNING HORIZON MAY BE IN YEARS,
MONTHS, DAY, HOURS, MINUTES, …
40. PROBLEMS
MODELS
OPTEX – DECISION SUPPORT SYSTEM ELEMENTS
A PROBLEM IS A COLLECTION OF CONSTRAINTS
A MODEL IS A COLLECTION OF PROBLEMS
CONNECTED BY A DATA FLOW AND A MODEL CONTROL
41. A DECISION SUPPORT SYSTEM IS A COLLECTION OF
MODELS AND DATA FLOW ALL USING THE SAME DATA MODEL
AND THE SAME FRAMEWORK
PTA
Aggregated Industrial
Operations
Tactical Plannings
DEM
Demand
Long/Medium/Short
Term
INV
Inventory
Policies
Demand Forecasting
Medium/Short Term
Demand Stages
Medium/Short Term
Inventory
Policies
Production
Goals
POD
Production
Scheduling
DIS
Distribution
scheduling
Distribution
Goals
PCO
Sourcing
Scheduling
Consumption
Goals
Production
Orders
Distriution
Orders
Purchase
Orders
PES
Supply Chain Design
Marjet Stages
Long/Medium Term
Expansion
Plans
DSS
DSS
MODELS
OPTEX – DECISION SUPPORT SYSTEMS ELEMENTS
42. ADVANCED OPTIMIZATION
INVESTMENTS COORDINATOR
INTERZONE
COORDINATOR
SECTOR 1
STOCHASTIC 1
INTERZONE
COORDINATOR
SECTOR 1
STOCHASTIC 1
INTERSECTOR OPERATIONS
COORDINATOR
STOCHASTIC CONDITION 1
DYNAMIC
COORD.
ZONA S.1
DYNAMIC
COORD.
ZONA S.ZS
DYNAMIC
COORD.
ZONE 1.1
DYNAMIC
COORD.
ZONA 1.Z1
1 T2 T-1 1 T2 T-1 1 T2 T-1 1 T2 T-1
TIME
PARTITION
INVESTMENTS
SECTOR
ZONE
DECOMPOSITION
MULTILEVEL
SYSTEM
INTERZONE
COORDINATOR
SECTOR 1
STOCHASTIC H
INTERZONE
COORDINATOR
SECTOR 1
STOCHASTIC H
INTERSECTOR OPERATIONS
COORDINATOR
STOCHASTIC CONDITION H
DYNAMIC
COORD.
ZONA S.1
DYNAMIC
COORD.
ZONA S.ZS
DYNAMIC
COORD.
ZONE 1.1
DYNAMIC
COORD.
ZONA 1.Z1
1 T2 T-1 1 T2 T-1 1 T2 T-1 1 T2 T-1
RANDOM
OPERATIONS
53. Scenario H
Scenario 1
Scenario 2
ARBOL DE DECISIONES DE
MULTIPLES ETAPAS
t = 1 t = 2 t = 3 t = 4
OPTEX- MULTISTAGE STOCHASTIC OPTIMIZATION
OPTEX HAS TOOLS ORIENTED TO DEVELOP
MULTISTAGE STOCHASTIC OPTIMIZATION MODELS
AUTOMATIC CONVERSION OF A
DETERMINISTIC MODEL INTO STOCHASTIC
MULTI-STAGE
DECISION TREE
54. MULTI-STAGE
DECISION TREE
N1
e = 1 e = 2 e = 3
t
1 13 25 36
N21
N22
N21
N22
N21
N22
N21
N22
Hidrology 1988
Hidrology 1992
Hidrology 1985
Hidrology 1990
High Demand High Price
Hidrology 1988 Low Demand Low Price
High Demand High Price
Low Demand Low PriceHidrology 1990
High Demand High Price
Low Demand Low PriceHidrology 1992
High Demand Low Price
High PriceLow Demand
High Demand High Price
Hidrology 1988
Hidrology 1988
0.125
0.0625
UNCERTAINTY DIMENSIONS
• Demand
• Fuel Prices
• Water Inflows
• Others
55. OPTEX HAS TOOLS ORIENTED TO DEVELOP
MULTISTAGE STOCHASTIC OPTIMIZATION
INCLUDING MULTIPLES TYPES OF RISK CONSTRAINTS
Conditional Value-at-Risk (CVaR)
Cost Probability Function
Desvío
Estándar
(s)
VaR
b=0.05
1.645 s
Cost - f(x|w)a(b)
f ( f(x|w) )
jb( f(x|w) )
OPTEX- MULTISTAGE STOCHASTIC OPTIMIZATION
57. DETERMINISTIC CASE
t = 1 t = 2
Mean
Demand
Deterministics
Investment
Decisions
Deterministics
Future Operations
Decisions
58. TWO-STAGE DECISION TREE FOR
DEMAND: UNCERTAINTY DIMENSION
t = 1 t = 2
Scenario
Demand 10
Scenario
Demand 1
Scenario
Demand 2
Deterministics
Investment
Decisions
0.10
0.10
Uncertainty
Future Operations
Decisions
59. Demand 10
Demand 1
Demand 2
0.10
0.10
Demand 10
Demand 1
Demand 2
0.10
0.10
WITHOUT Extrem Event
0.90
0.10
t = 1 t = 2
Deterministics
Investment
Decisions Uncertainty
Future Operations
Decisions
TWO-STAGE DECISION TREE FOR
DEMAND: UNCERTAINTY DIMENSION 1
EXTREME EVENT: UNCERTAINTY DIMENSION 2
WITH Extrem Event
60. THE AUTOMATIC CONVERSION IMPLIES:
1. TO INCLUDE THE INDEXES RELATED WITH THE
UNCERTAINTY DIMENSIONS
61. THE AUTOMATIC CONVERSION IMPLIES:
2. TO DEFINE A DECISION TREE
3. TO SPECIFY THE NON ANTICIPATIVE VARIABLES
4. TO SPECIFY THE PARAMETERS WITH THE
UNCERTAINTY DIMENSIONS
2.
3.
4.
84. OPTEX – C DSS PROGRAM STRUCTURE
I/O
Routines
MODELs
Routines
Main
OPTEX-COINLP
LINK
Routine
COINLP
Routines
CPLEX
Routines
CONSTRAINTs
Routines
OPTEX-CPLEX
LINK
Routine
OPTEX-xxxxx
LINK
Routine
XXXXX
Routines
PROBLEMs
Routines
LARGE SCALE OPTIMIZATION
Routines
DSS.LIB or DSS.DLL
DSS
DATABASE
85. OPTEX – C DSS PROGRAM STRUCTURE
MODELs
Routines
OPTEX-COINLP
LINK
Routine
COINLP
Routines
CPLEX
Routines
CONSTRAINTs
Routines
OPTEX-CPLEX
LINK
Routine
OPTEX-xxxxx
LINK
Routine
XXXXX
Routines
PROBLEMs
Routines
LARGE SCALE OPTIMIZATION
Routines
DSS.LIB or DSS.DLL
DSS
DATABASE
USER
Routines
OPTEX-USER
LINK
Routine
Customized Visual User Interface
USER
ERP
115. INFORMATION
SYSTEM
Min t j h CTt(GTjth)
sujeto a:
GDzth - uTN(z) LDuzth = 0
GDzth + GHAzth + DEFzth = DEMzth
ENuth - jL1(u) GTEjuth
- vL2(u) LLvuth = 0
Sistema Descripción
Capacidad
Térmica (MW)
EEB.
ISA.
EPM
COR
Energía Eléctrica de Bogotá
Interconexión Eléctrica S.A.
Empresas Públicas de Medellín
CORELCA
45
67
0
78
MATHEMATICAL MODEL
INFORMATION SYSTEM
INDUSTRIAL DATA
INFORMATION SYSTEM
117. RELACIÓN SIMM - SIDI
INDEX
Parameter
Restricción
IndexesVariable
Indexes
Indexes
ENTITY
ENTITIES
RELATIONS
SIMM:
MATHEMATICAL
MODEL
INFORMATION
SYSTEM
SIDI:
INDUSTRIAL
DATA
INFORMATION
SYSTEM
IndexesSets
118. IN OPTEX THE IMPLEMENTATION OF THE
INDUSTRIAL DATA INFORMATION SYSTEM IS
BASED IN A FILLING THE BLANKS GUIDED
PROCESS, SIMILAR TO THE PROCESS TO
IMPLEMENTATION OF THE MATHEMATICAL
MODELS.
THE MODELER DOESN’T NEED TO BE AN SPECIALIST
IN DATABASES LANGUAGES AND INFORMATION
SYSTEMS
IMPLEMENTATION INDUSTRIAL DATA INFORMATION SYSTEM
119. IN OPTEX THE IMPLEMENTATION OF THE
INDUSTRIAL DATA INFORMATION SYSTEM IS
BASED IN A FILLING THE BLANKS GUIDED
PROCESS, SIMILAR TO THE PROCESS TO
IMPLEMENTATION OF THE MATHEMATICAL
MODELS.
THE MODELER DOESN’T NEED TO BE AN SPECIALIST
IN DATABASES LANGUAGES AND INFORMATION
SYSTEMS
IMPLEMENTATION INDUSTRIAL DATA INFORMATION SYSTEM
122. INDUSTRIAL DATA
INFORMATION SYSTEM
IS A COLLECTION OF:
DATA TABLES, SHELL WINDOWS, DATA
WINDOWS AND MENUS ORIENTED TO THE
FINAL USER
INDUSTRIAL DATA INFORMATION SYSTEM
123. INDUSTRIAL DATA INFORMATION SYSTEM
THE DATABASE OF THE INFORMATION SYSTEM
IS A COLLECTION OF RELATIONAL DATA TABLES
ORIENTED TO MANAGE LARGE AMOUNT OF DATA, LIKE IN
THE REAL WORLD MODELS.
124. OPTEX GENERATES, ON-LINE, DATA
WINDOWS WITH A COLLECTION OF
WINDOWS-TOOLS THAT HELP THE USER IN
THE LABOR OF DATA CAPTURE.
THE DATA WINDOWS ARE JOINT IN A SHELL
WINDOWS IN A RELATIONAL APPROACH.
INDUSTRIAL DATA INFORMATION SYSTEM
125. HIERARCHIC INFORMATION SYSTEM FOR MODELS RESULTS
SCENARIO FAMILY
ROOT DIRECTORY
Family
No. 1
Directory
Family
No. E
Directory
Family
No. n
Directory
Scenario
No. E-X
Directory
Scenario
No. E-X
Directory
Tables
Parameters
Tables
Resulting
Parameters
Tables
Variable
Results
Tables
Parameters
Results
Tables
Variable
Results
Scenario
No. E-X
Directory
Tables
Parameter
Results
Tables
Variable
Results
AUTOMATICALLY, OPTEX GENERATES A HIERARCHIC INFORMATION
SYSTEM TO STORE THE RESULTS OF THE MODELS USING THE
CONCEPTS OF SCENARIOS AND FAMILY OF SCENARIOS.
126. OPTEX STORES IN TABLES
THE MATRIX AND THE
VECTORS RESULT OF THE
MATRIX GENERATION.
THIS ALLOWS THE
DEVELOPER TO VISUALIZE
AND CHECK THE VALIDITY
OF HIS MODELING
INDUSTRIAL DATA INFORMATION SYSTEM
127. OPTEX STORES
THE RESULTS
IN DATA
TABLES
AND/OR IN
TEXT FILES
AND/OR IN
EXCEL FILES
INDUSTRIAL DATA INFORMATION SYSTEM
128. OPTEX STORES
THE RESULTS
IN DATA
TABLES
AND/OR IN
TEXT FILES
AND/OR IN
EXCEL FILES
INDUSTRIAL DATA INFORMATION SYSTEM
129. OPTEX STORES
THE RESULTS
IN DATA
TABLES
AND/OR IN
TEXT FILES
AND/OR IN
EXCEL FILES
INDUSTRIAL DATA INFORMATION SYSTEM
130. OPTEX PROVIDES TOOLS
FOR VISUALIZATION OF
LITTLE MODELS.
FOR LARGE SCALE MODELS
THE STRUCTURE OF
RESULTS TABLES ARE
ORIENTED TO USE IN
MULTIDIMENSIONAL
ANALYSIS DATA TOOLS
INDUSTRIAL DATA INFORMATION SYSTEM
161. To capitalize its expertise in mathematical optimization projects,
DW created OPCHAIN, a brand through which we have grouped
the solutions developed by DW, in different areas of application
using mathematical programming methodologies and technologies.
In 2012, OPCHAIN has accumulated the experience of more than
thirty-five (35) years in engineering problem solving and business
analytics using mathematical programming models. OPCHAIN
models are fully programmable, easy to customize for each client,
and are easily integrated with other IT solutions in organizations.
OPCHAIN
OPTIMIZING THE VALUE CHAIN
162. OPCHAIN-SCO
SUPPLY CHAIN OPTIMIZATION
OPCHAIN-TSO
TRANSPORT SYSTEMS OPTIMIZATION
OPCHAIN-RSO
RETAIL CHAIN OPTIMIZATION
OPCHAIN-RPO
REGIONAL PLANING OPTIMIZATION
OPCHAIN-ESO
ENERGY SYSTEMS OPTIMIZATION
OPCHAIN-BANK
BANK SYSTEMS OPTIMIZATION
OPCHAIN-EDO
EDUCATIONAL SYSTEMS OPTIMIZATION
OPCHAIN-MINES
MINES SYSTEMS OPTIMIZATION
163. OPTEX Mathematical Modeling System,
was developed to support
DecisionWare’s mathematical modeling
projects since 1991.
OPTEX supports the development of all
multi-model OPCHAIN-DSS developed
by
164. SERVICES
TO SELL OPTEX MATHEMATICAL MODELING MANAGEMENT SYSTEM
TO SELL OPCHAIN-MODELS IN ANY PLATFORM
(INCLUDING SOURCE CODE)
TO CONVERT MODELS FROM ANY PLATFORM TO ANY PLATFORM
TO DEVELOPMENT ON DEMAND MODELS IN ANY PLATFORM
ON DEMAND OPTIMIZATION IN THE CLOUD