Slides for my talk at Scala By The Bay 2015 http://scalabythebay2015.sched.org/event/8cbbc52523ac9e5a905361e00d357099
GNU Mathprog is an algebraic modeling language for describing mathematical programming models. The language is a subset of AMPL, the most popular language used for describing such models. One advantage of this languages is the similarity of its syntax with the mathematical notation used to describe optimization problems. In this talk we describe the use of Scala in the construction of an embedded DSL to work with Mathprog models. At construction time, the DSL works as an alternative way to build the AST representing the model, taking advantage of the Scala compiler to check the validity of most expressions. The AST can be created also by parsing Mathprog model files. Once an AST is obtained, the DSL allows to manipulate and transform the AST to extract data or create new models. We show how the use of type classes and implicit conversion prioritization gives us a systematic way to define operators, valid combinations of parameters and result types, and conversions between expressions.
https://github.com/gerferra/amphip
This document provides an overview of Abide, a static checker tool for Scala based on type-checked ASTs. It has the goals of unifying existing rule-based approaches, making it easy to write and use rules, and providing reasonable performance and good integrations. Rules are decoupled from the tool and can come from core, extra, or project-specific packages. The document gives examples of core rules and rules for Akka and describes Abide's architecture, use, anatomy including traversers, directives, and how rules are coded. It encourages contributing rules to help the value of the tool.
Relational Databases are Evolving To Support New Data CapabilitiesEDB
A commissioned study conducted by Forrester Consulting on behalf of EnterpriseDB, published in January 2015, presents a case study for the evolution of relational database management systems. The study, Relational Databases are Evolving to Support New Data Capabilities, found that the majority—78%—of database decisions makers wanted one solution that could handle relational and NoSQL data types.
The study finds that relational databases are evolving to address the needs of end users seeking to link unstructured and structured data types and that decision makers should look to invest in these solutions. EDB’s Postgres Plus Advanced Server, for example, addresses these needs with such capabilities as support for unstructured data types, non-durable tables, tools for large-scale data loads, and integration technologies that connect standalone NoSQL solutions with Postgres.
The document discusses how code is often wrong due to unanticipated inputs, changing requirements, and bugs. It advocates embracing the idea that "your code is wrong" to design more robust software through principles like measuring inputs, monitoring systems, embracing immutability, minimizing dependencies, respecting functional ranges, and embracing recomputation to handle changing needs. The document uses examples from Storm and other systems to illustrate these principles for building software that can withstand failures and remain operational.
This document summarizes Gordon Rowell's talk about Puppet deployment at Google. Puppet is used to manage internal laptops, desktops, and servers but not customer-facing infrastructure. It manages "lots" of Mac/Ubuntu nodes and "tens" of Puppet servers deployed across globally distributed virtual IPs. Scaling Puppet at Google involves deploying redundant server clusters with Anycast routing clients to the nearest cluster. Load balancing challenges include ensuring enough capacity and routing if clusters fail. Thundering herds of nodes checking in simultaneously and releasing new OS and addon versions on different tracks also create Puppet challenges.
Spark is an alternative to Hadoop MapReduce for processing large datasets in parallel across a cluster, but it is not an alternative to Hadoop itself. While Spark can handle very large datasets up to 200 PB, it does not require that much memory and can work with mutable data. Spark supports both Scala and Java APIs and can also be used alongside existing Hadoop technologies like Hive, Pig, Impala, Tez and Drill.
This document provides an introduction to linear and integer programming. It defines key concepts such as linear programs (LP), integer programs (IP), and mixed integer programs (MIP). It discusses the complexity of different optimization problem types and gives examples of LP and IP formulations. It also covers common techniques for solving LPs and IPs, including the simplex method, cutting plane methods, branch and bound, and heuristics like beam search.
Have you heard that all in-memory databases are equally fast but unreliable, inconsistent and expensive? This session highlights in-memory technology that busts all those myths.
Redis, the fastest database on the planet, is not a simply in-memory key-value data-store; but rather a rich in-memory data-structure engine that serves the world’s most popular apps. Redis Labs’ unique clustering technology enables Redis to be highly reliable, keeping every data byte intact despite hundreds of cloud instance failures and dozens of complete data-center outages. It delivers full CP system characteristics at high performance. And with the latest Redis on Flash technology, Redis Labs achieves close to in-memory performance at 70% lower operational costs. Learn about the best uses of in-memory computing to accelerate everyday applications such as high volume transactions, real time analytics, IoT data ingestion and more.
Bba 3274 qm week 10 integer programmingStephen Ong
This document discusses integer programming and various types of integer programming problems that commonly arise in business. It provides an example of a pure integer programming problem involving production planning at a company that makes chandeliers and ceiling fans. The document also discusses mixed-integer programming problems and modeling problems using 0-1 variables, providing examples for capital budgeting and facility location problems. Various software packages are demonstrated for solving integer programming problems.
The document provides an overview of asynchronous processing and how it relates to scalability and performance. It discusses key topics like sync vs async, scheduling, latency measurement, concurrent vs lock-free vs wait-free data structures, I/O models like IO, AIO, NIO, zero-copy, and sorting algorithms. It emphasizes picking the right tools for the job and properly benchmarking and measuring performance.
This document discusses integer programming and methods for solving integer programming problems. It begins with an introduction to integer programming models, including total integer models, 0-1 integer models, and mixed integer models. It then provides examples of each type of integer programming model. The document also describes traditional approaches for solving integer programming problems, including the branch and bound method and Gomory cutting plane method. It provides an example demonstration of applying the Gomory cutting plane method.
This document provides an introduction and overview of integer programming problems. It discusses different types of integer programming problems including pure integer, mixed integer, and 0-1 integer problems. It provides examples to illustrate how to formulate integer programming problems as mathematical models. The document also discusses common solution methods for integer programming problems, including the cutting-plane method. An example of the cutting-plane method is provided to demonstrate how it works to find an optimal integer solution.
Delivering Meaning In Near-Real Time At High Velocity In Massive Scale with A...Helena Edelson
Streaming Big Data: Delivering Meaning In Near-Real Time At High Velocity At Massive Scale with Apache Spark, Apache Kafka, Apache Cassandra, Akka and the Spark Cassandra Connector. Why this pairing of technologies and How easy it is to implement. Example application: https://github.com/killrweather/killrweather
The document discusses monads and functional programming concepts. It begins by explaining that monads are structures that put values in computational contexts. It then provides a technical definition of a monad involving endofunctors, natural transformations, and laws. Several examples are given to illustrate monads, including the Optional monad in Java to handle null values, and the Stream monad to represent sequences. The document advocates using monads to make aspects like errors, state, and effects explicit in a program's type system.
NewSQL overview:
- History of RDBMs
- The reasons why NoSQL concept appeared
- Why NoSQL was not enough, the necessity of NewSQL
- Characteristics of NewSQL
- 7 DBs that belongs to NewSQL
- Overview Table with main properties
The new Actor representation in Akka Typed allows formulations that lend themselves to monadic interpretation or introspection. This leads us to explore possibilities for expressing and verifying dynamic properties like the adherence to a communication protocol between multiple agents as well as the safety properties of that protocol on a global level. Academic research in this area is far from complete, but there are interesting initial results that we explore in this session: precisely how much purity and reasoning can we bring to the distributed world?
- Scala originated from Martin Odersky's work on functional programming languages like OCaml in the 1980s and 1990s. It was designed to combine object-oriented and functional programming in a practical way.
- Key aspects of Scala include being statically typed while also being flexible, its unified object and module system, and treating libraries as primary over language features.
- Scala has grown into an large ecosystem centered around the JVM and also targets JavaScript via Scala.js. Tooling continues to improve with faster compilers and new IDE support.
- Future work includes establishing TASTY as a portable intermediate representation, connecting Scala to formal foundations through the DOT calculus, and exploring new type
Espresso: LinkedIn's Distributed Data Serving Platform (Paper)Amy W. Tang
This paper, written by the LinkedIn Espresso Team, appeared at the ACM SIGMOD/PODS Conference (June 2013). To see the talk given by Swaroop Jagadish (Staff Software Engineer @ LinkedIn), go here:
http://www.slideshare.net/amywtang/li-espresso-sigmodtalk
This presentation was first given at INFORMS in November 2013. It presents an analysis of the features that had the most impact on MIP solver performance during the last 12 years.
More presentations are available at https://www.ibm.com/developerworks/community/groups/community/DecisionOptimization
(video of these slides available here http://fsharpforfunandprofit.com/fppatterns/)
In object-oriented development, we are all familiar with design patterns such as the Strategy pattern and Decorator pattern, and design principles such as SOLID.
The functional programming community has design patterns and principles as well.
This talk will provide an overview of some of these, and present some demonstrations of FP design in practice.
Concurrency: The Good, The Bad and The Uglylegendofklang
This document compares different approaches to concurrency and distributed computing, including threads with locks, executors, software transactional memory (STM), futures, actors, dataflow, asynchronous programming, reactive programming, and functional reactive programming (FRP). For each approach, it outlines the good qualities, potential bad qualities, and ugly issues to watch out for. In the conclusion, it recommends picking approaches tailored to each problem domain and system requirements.
The document describes the two phase simplex method for solving linear programming problems (LPP) with greater than or equal to constraints. In phase I, an auxiliary LPP is constructed by introducing artificial variables and slack/surplus variables. The objective is to minimize the artificial variables using the simplex method. If an artificial variable is positive or zero in the optimal basis, phase II is executed to solve the original LPP. Phase I provides a starting feasible solution for phase II to obtain the actual optimal solution.
This is a Community Spotlight that was presented by EDNON for the OpenMetadata Community Meetup held May 21st, 2025. Meetups are happening every month! For more information, go to https://www.meetup.com/openmetadata-meetup-group/
Learn how EDNON is transforming its data governance with OpenMetadata. This presentation will cover key milestones from their work with OpenMetadata over the past two years, including infrastructure deployment, data source integration, business glossary definition, and the implementation of data quality policies. See how they're using OpenMetadata to automate documentation, ensure BI product quality, and democratize data access.
Top Reasons to Hire Dedicated Odoo Developers for Your ERP ProjectKanak Infosystems LLP.
Searching for ways to enhance your business operations with Odoo ERP? Look no further. This in-depth overview explains why hiring dedicated Odoo developers is a smart investment for businesses of all sizes. Learn how expert developers can tailor Odoo modules to match your business processes, integrate third-party applications, and maintain long-term system performance. From boosting team productivity to improving customer experience, this document highlights how Odoo developers play a vital role in turning your ERP vision into a fully functional, ROI-driven reality.
Scaling up your Snapshot tests, without the frictionarnold844201
We talk about why most companies give up on snapshot tests, and how you can improve your tooling to scale up your snapshot testing (and testing) efforts
OpenMetadata Community Meeting - 21st May 2025OpenMetadata
The community meetup was held Wednesday April 16, 2025 @ 9:00 AM PST.
Catch the next OpenMetadata Community Meetup @ https://www.meetup.com/openmetadata-meetup-group/
In this month's OpenMetadata Community Meetup, "Embedding an MCP Server into OpenMetadata: Empowering AI-driven Automation," you will learn how Model Context Protocol (MCP) is revolutionizing how AI agents interact with metadata, and its possibilities for automation and intelligent data management within OpenMetadata. Join us to explore the future of AI-driven data operations.
Agenda Highlights:
👋 Introducing MCP - An open protocol for AI agent communication.
🦾 Enabling AI Metadata Interaction: Unified interface for AI to use metadata.
🌯 Embedded MCP Server: Why we are embedding MCP into OMD.
🥳 MCP + OMD Use Cases: Description Completeness, and Pipeline Failure Reports.
🎯 Updating Assets: AI-driven updates to OpenMetadata assets.
➕ And More!
Steps to Develop a KuCoin Clone Script.pptxriyageorge2024
Learn the essential steps to develop a KuCoin-like crypto exchange using a clone script. AppcloneX offers a secure, customizable KuCoin clone script to launch your platform quickly and efficiently.
Modern Software Testing Playwright, Gen AI, and Machine Learning Models for E...Venkatesh (Rahul Shetty)
Playwright, Gen AI, and machine learning models are used in modern software testing to make the process quicker and more intelligent. Venkatesh (Rahul Shetty) provides innovative techniques to increase the effectiveness of testing. Visit our website at https://rahulshettyacademy.com/ to find out more about us.
Download Link 👇
https://techblogs.cc/dl
LightBurn Crack is a powerful and versatile software for laser engraving and cutting that is designed to work with a wide variety of ...
The document provides an overview of asynchronous processing and how it relates to scalability and performance. It discusses key topics like sync vs async, scheduling, latency measurement, concurrent vs lock-free vs wait-free data structures, I/O models like IO, AIO, NIO, zero-copy, and sorting algorithms. It emphasizes picking the right tools for the job and properly benchmarking and measuring performance.
This document discusses integer programming and methods for solving integer programming problems. It begins with an introduction to integer programming models, including total integer models, 0-1 integer models, and mixed integer models. It then provides examples of each type of integer programming model. The document also describes traditional approaches for solving integer programming problems, including the branch and bound method and Gomory cutting plane method. It provides an example demonstration of applying the Gomory cutting plane method.
This document provides an introduction and overview of integer programming problems. It discusses different types of integer programming problems including pure integer, mixed integer, and 0-1 integer problems. It provides examples to illustrate how to formulate integer programming problems as mathematical models. The document also discusses common solution methods for integer programming problems, including the cutting-plane method. An example of the cutting-plane method is provided to demonstrate how it works to find an optimal integer solution.
Delivering Meaning In Near-Real Time At High Velocity In Massive Scale with A...Helena Edelson
Streaming Big Data: Delivering Meaning In Near-Real Time At High Velocity At Massive Scale with Apache Spark, Apache Kafka, Apache Cassandra, Akka and the Spark Cassandra Connector. Why this pairing of technologies and How easy it is to implement. Example application: https://github.com/killrweather/killrweather
The document discusses monads and functional programming concepts. It begins by explaining that monads are structures that put values in computational contexts. It then provides a technical definition of a monad involving endofunctors, natural transformations, and laws. Several examples are given to illustrate monads, including the Optional monad in Java to handle null values, and the Stream monad to represent sequences. The document advocates using monads to make aspects like errors, state, and effects explicit in a program's type system.
NewSQL overview:
- History of RDBMs
- The reasons why NoSQL concept appeared
- Why NoSQL was not enough, the necessity of NewSQL
- Characteristics of NewSQL
- 7 DBs that belongs to NewSQL
- Overview Table with main properties
The new Actor representation in Akka Typed allows formulations that lend themselves to monadic interpretation or introspection. This leads us to explore possibilities for expressing and verifying dynamic properties like the adherence to a communication protocol between multiple agents as well as the safety properties of that protocol on a global level. Academic research in this area is far from complete, but there are interesting initial results that we explore in this session: precisely how much purity and reasoning can we bring to the distributed world?
- Scala originated from Martin Odersky's work on functional programming languages like OCaml in the 1980s and 1990s. It was designed to combine object-oriented and functional programming in a practical way.
- Key aspects of Scala include being statically typed while also being flexible, its unified object and module system, and treating libraries as primary over language features.
- Scala has grown into an large ecosystem centered around the JVM and also targets JavaScript via Scala.js. Tooling continues to improve with faster compilers and new IDE support.
- Future work includes establishing TASTY as a portable intermediate representation, connecting Scala to formal foundations through the DOT calculus, and exploring new type
Espresso: LinkedIn's Distributed Data Serving Platform (Paper)Amy W. Tang
This paper, written by the LinkedIn Espresso Team, appeared at the ACM SIGMOD/PODS Conference (June 2013). To see the talk given by Swaroop Jagadish (Staff Software Engineer @ LinkedIn), go here:
http://www.slideshare.net/amywtang/li-espresso-sigmodtalk
This presentation was first given at INFORMS in November 2013. It presents an analysis of the features that had the most impact on MIP solver performance during the last 12 years.
More presentations are available at https://www.ibm.com/developerworks/community/groups/community/DecisionOptimization
(video of these slides available here http://fsharpforfunandprofit.com/fppatterns/)
In object-oriented development, we are all familiar with design patterns such as the Strategy pattern and Decorator pattern, and design principles such as SOLID.
The functional programming community has design patterns and principles as well.
This talk will provide an overview of some of these, and present some demonstrations of FP design in practice.
Concurrency: The Good, The Bad and The Uglylegendofklang
This document compares different approaches to concurrency and distributed computing, including threads with locks, executors, software transactional memory (STM), futures, actors, dataflow, asynchronous programming, reactive programming, and functional reactive programming (FRP). For each approach, it outlines the good qualities, potential bad qualities, and ugly issues to watch out for. In the conclusion, it recommends picking approaches tailored to each problem domain and system requirements.
The document describes the two phase simplex method for solving linear programming problems (LPP) with greater than or equal to constraints. In phase I, an auxiliary LPP is constructed by introducing artificial variables and slack/surplus variables. The objective is to minimize the artificial variables using the simplex method. If an artificial variable is positive or zero in the optimal basis, phase II is executed to solve the original LPP. Phase I provides a starting feasible solution for phase II to obtain the actual optimal solution.
This is a Community Spotlight that was presented by EDNON for the OpenMetadata Community Meetup held May 21st, 2025. Meetups are happening every month! For more information, go to https://www.meetup.com/openmetadata-meetup-group/
Learn how EDNON is transforming its data governance with OpenMetadata. This presentation will cover key milestones from their work with OpenMetadata over the past two years, including infrastructure deployment, data source integration, business glossary definition, and the implementation of data quality policies. See how they're using OpenMetadata to automate documentation, ensure BI product quality, and democratize data access.
Top Reasons to Hire Dedicated Odoo Developers for Your ERP ProjectKanak Infosystems LLP.
Searching for ways to enhance your business operations with Odoo ERP? Look no further. This in-depth overview explains why hiring dedicated Odoo developers is a smart investment for businesses of all sizes. Learn how expert developers can tailor Odoo modules to match your business processes, integrate third-party applications, and maintain long-term system performance. From boosting team productivity to improving customer experience, this document highlights how Odoo developers play a vital role in turning your ERP vision into a fully functional, ROI-driven reality.
Scaling up your Snapshot tests, without the frictionarnold844201
We talk about why most companies give up on snapshot tests, and how you can improve your tooling to scale up your snapshot testing (and testing) efforts
OpenMetadata Community Meeting - 21st May 2025OpenMetadata
The community meetup was held Wednesday April 16, 2025 @ 9:00 AM PST.
Catch the next OpenMetadata Community Meetup @ https://www.meetup.com/openmetadata-meetup-group/
In this month's OpenMetadata Community Meetup, "Embedding an MCP Server into OpenMetadata: Empowering AI-driven Automation," you will learn how Model Context Protocol (MCP) is revolutionizing how AI agents interact with metadata, and its possibilities for automation and intelligent data management within OpenMetadata. Join us to explore the future of AI-driven data operations.
Agenda Highlights:
👋 Introducing MCP - An open protocol for AI agent communication.
🦾 Enabling AI Metadata Interaction: Unified interface for AI to use metadata.
🌯 Embedded MCP Server: Why we are embedding MCP into OMD.
🥳 MCP + OMD Use Cases: Description Completeness, and Pipeline Failure Reports.
🎯 Updating Assets: AI-driven updates to OpenMetadata assets.
➕ And More!
Steps to Develop a KuCoin Clone Script.pptxriyageorge2024
Learn the essential steps to develop a KuCoin-like crypto exchange using a clone script. AppcloneX offers a secure, customizable KuCoin clone script to launch your platform quickly and efficiently.
Modern Software Testing Playwright, Gen AI, and Machine Learning Models for E...Venkatesh (Rahul Shetty)
Playwright, Gen AI, and machine learning models are used in modern software testing to make the process quicker and more intelligent. Venkatesh (Rahul Shetty) provides innovative techniques to increase the effectiveness of testing. Visit our website at https://rahulshettyacademy.com/ to find out more about us.
Download Link 👇
https://techblogs.cc/dl
LightBurn Crack is a powerful and versatile software for laser engraving and cutting that is designed to work with a wide variety of ...
Admin, Product & Beyond with FilamentPHP.pptxeastonmeth
When starting a new project, there is often a lot of monotonous work that is involved in getting your frontend components and backend logic and validation set up. In reality, a lot of this work is the same across every project, and there isn't an inherit need to rewrite the same thing every single project. That's where FilamentPHP comes in.
In this talk, we will go over the basics of getting started with FilamentPHP and building a basic admin panel to show how streamlined it makes the process. We will also go over some of the capabilities it has outside of panel-based applications, and how you can implement it in your existing project.
So come along, bring a friend, and learn how to make your life easier with FilamentPHP!
Linux Improvements in Memory Corruption Based ProtectionsVlatko Kosturjak
Linux Improvements in Memory Corruption Based Protections presented at DORS/CLUC 2025, Zagreb, Croatia.
Intel Indirect Branch Tracking (IBT) in Linux.
Intel Shadow Stack (SS) implementation in Linux.
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IObit Uninstaller Pro Crack is a program that helps you fully eliminate any unwanted software from your computer to free up disk space and improve ...
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(Ethical) Alternatives to Piracy: A Quick Guide to Free and Open Source Softw...s-m-quadri
This presentation, titled "Ethical Alternatives to Piracy", originates from a scholarly effort at Deogiri Institute of Engineering and Management Studies. It addresses the urgent ethical, legal, and cybersecurity challenges posed by the widespread use of pirated software. Piracy is defined as the unauthorized use of paid digital content, and it is a practice fraught with risks—ranging from legal consequences and data breaches to malware infections, software crashes, and absence of support or updates. Alarming statistics indicate that 91% of users in India engage in piracy, a number that has surged due to increased digital dependence in the post-pandemic era.
The presentation transitions from critique to constructive solution by introducing reliable, free, and ethical alternatives via Free and Open Source Software (FOSS) and free operating systems. Emphasizing India's moral and cultural ethos, the speaker argues for a shift from piracy to integrity-driven digital usage, especially among engineering professionals. Two robust Linux-based alternatives to Windows are discussed: Ubuntu Studio, aimed at creatives with its XFCE desktop and Long Term Support (LTS) stability; and Pop!_OS, designed for STEM users, offering a GNOME desktop that balances performance with usability.
A broad selection of FOSS tools is explored as viable substitutes for expensive proprietary software. These include Blender (3D animation; alternative to Maya), Natron (compositing; alternative to After Effects), DaVinci Resolve (professional-grade editing), Kdenlive (video editing), VLC (media playback and conversion), OBS Studio (screen recording and streaming), and Audacity or Ardour (audio editing and mixing). Visual design needs are met by Inkscape and GIMP, serving as counterparts to Adobe Illustrator and Photoshop, respectively. Productivity is addressed through LibreOffice as an alternative to Microsoft Office, and tools like VS Code, Android Studio, VirtualBox, Synaptic, and Stacer round out the system management and development environment options.
The central argument is not to vilify proprietary software, but to discourage unethical use through piracy. FOSS alternatives offer a legitimate, secure, and often equally powerful path for students, professionals, and institutions. The presentation concludes by encouraging a transition to open-source ecosystems—not just as a technical decision, but as a step toward ethical, sustainable, and secure digital practice. It is a call to adopt responsible engineering values and become part of the global open-source movement.
POS Testing Strategies and Best Practicesjamescantor38
Learn about various POS testing techniques including functional, regression, integration, and security testing. Explore industry best practices for delivering reliable POS systems.
Menu in Android (Define,Create,Inflate and Click Handler)Nabin Dhakal
In Android, a **menu** provides options for user actions and navigation in an app. Menus can appear as **options menus** (accessed via the app bar), **context menus** (triggered by long-press), or **popup menus** (small floating lists). They are typically defined in XML using `<menu>` and `<item>` tags and inflated using `MenuInflater` in activities or fragments. Developers handle menu item clicks using `onOptionsItemSelected()` or similar methods. Menus help improve usability by grouping actions in a consistent interface. Common use cases include settings, search, and sharing options, offering a clean and accessible way to enhance app functionality.
Menus in Android offer a consistent and user-friendly way to present actions and navigation options within an app. By using options menus for global actions, context menus for specific UI elements, and popup menus for flexible interaction, developers can enhance the overall usability and functionality of their applications. Proper implementation of menus not only organizes actions effectively but also improves the user experience by making key features easily accessible.
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Grand Theft Auto 6 PC Game Cracked Full Setup Download. The Grand Theft Auto arrangement has consistently been celebrated for utilizing inside.
We’re honored to share the official keynote presentation that opened CFCamp 2025, led by Luis Majano, creator of ColdBox, BoxLang, and CEO of Ortus Solutions.
This PDF features the full slide deck from Day 1’s keynote, where Luis presented a powerful vision for the future of modern CFML development, highlighted the evolution of BoxLang, and shared how Ortus is helping shape a dynamic future for developers around the world.
A heartfelt thank you to the CFCamp team for the opportunity to lead the keynote and showcase the innovation, community, and open source spirit driving the next chapter of CFML. 🚀
Best Practices Salesforce Training & Documentation.pptxMichael Orias
An embedded DSL to manipulate MathProg Mixed Integer Programming models within Scala
1. Germán Ferrari
Instituto de Computación, Universidad de la República, Uruguay
An embedded DSL to
manipulate MathProg
Mixed Integer
Programming
models within Scala
An embedded DSL to
manipulate MathProg
Mixed Integer
Programming
models within Scala
Germán Ferrari
Instituto de Computación, Universidad de la República, Uruguay
2. GNU MathProg
MathProg = Mathematical Programming
Has nothing to do with computer programming
It’s a synonym of “mathematical optimization”
A “program” here refers to a plan or schedule
3. GNU MathProg
It’s a modeling language
Optimization problem
Mathematical modeling
GNU MathProg
Computational experiments
Supported by the GNU Linear Programming Kit
4. GNU MathProg
Supports linear models, with either real or
integer decision variables
This is called “Mixed Integer Programming”
(MIP) because it mixes integer and real
variables
5. Generic MIP optimization problem
minimize f:
sum{j in J} c[j] * x[j] +
sum{k in K} d[k] * y[k];
subject to g{i in I}:
sum{j in J} a[i,j] * x[j] +
sum{k in K} e[i,k] * y[k] <= b[i];
6. Generic MIP optimization problem
Find x[j] and y[k], that minimize a function f,
satisfying constraints g[i]
minimize f:
sum{j in J} c[j] * x[j] +
sum{k in K} d[k] * y[k];
subject to g{i in I}:
sum{j in J} a[i,j] * x[j] +
sum{k in K} e[i,k] * y[k] <= b[i];
7. Generic MIP optimization problem
Find x[j] and y[k], that minimize a function f,
satisfying constraints g[i]
minimize f:
sum{j in J} c[j] * x[j] +
sum{k in K} d[k] * y[k];
subject to g{i in I}:
sum{j in J} a[i,j] * x[j] +
sum{k in K} e[i,k] * y[k] <= b[i];
Decision variables
(real or integer)
8. Generic MIP optimization problem
Find x[j] and y[k], that minimize a function f,
satisfying constraints g[i]
minimize f:
sum{j in J} c[j] * x[j] +
sum{k in K} d[k] * y[k];
subject to g{i in I}:
sum{j in J} a[i,j] * x[j] +
sum{k in K} e[i,k] * y[k] <= b[i];
Decision variables
(real or integer)
Objective
function
9. Generic MIP optimization problem
Find x[j] and y[k], that minimize a function f,
satisfying constraints g[i]
minimize f:
sum{j in J} c[j] * x[j] +
sum{k in K} d[k] * y[k];
subject to g{i in I}:
sum{j in J} a[i,j] * x[j] +
sum{k in K} e[i,k] * y[k] <= b[i];
Decision variables
(real or integer)
Objective
function
Constraints
10. Generic MIP optimization problem
Find x[j] and y[k], that minimize a function f,
satisfying constraints g[i]
minimize f:
sum{j in J} c[j] * x[j] +
sum{k in K} d[k] * y[k];
subject to g{i in I}:
sum{j in J} a[i,j] * x[j] +
sum{k in K} e[i,k] * y[k] <= b[i];
Decision variables
(real or integer)
Objective
function
Constraints
Linear
11. Generic MIP optimization problem
Full model
param m; param n; param l;
set I := 1 .. m;
set J := 1 .. n;
set K := 1 .. l;
param c{J}; param d{K};
param a{I, J}; param e{I, K}; param b{I};
var x{J} integer, >= 0;
var y{K} >= 0;
minimize f: sum{j in J} c[j] * x[j] + sum{k in K} d[k] * y[k];
subject to g{i in I}:
sum{j in J} a[i,j] * x[j] + sum{k in K} e[i,k] * y[k] <= b[i];
12. param m; param n; param l;
set I := 1 .. m;
set J := 1 .. n;
set K := 1 .. l;
param c{J}; param d{K};
param a{I, J}; param e{I, K}; param b{I};
var x{J} integer, >= 0;
var y{K} >= 0;
minimize f: sum{j in J} c[j] * x[j] + sum{k in K} d[k] * y[k];
subject to g{i in I}:
sum{j in J} a[i,j] * x[j] + sum{k in K} e[i,k] * y[k] <= b[i];
Generic MIP optimization problem
Declaration of
variables
13. param m; param n; param l;
set I := 1 .. m;
set J := 1 .. n;
set K := 1 .. l;
param c{J}; param d{K};
param a{I, J}; param e{I, K}; param b{I};
var x{J} integer, >= 0;
var y{K} >= 0;
minimize f: sum{j in J} c[j] * x[j] + sum{k in K} d[k] * y[k];
subject to g{i in I}:
sum{j in J} a[i,j] * x[j] + sum{k in K} e[i,k] * y[k] <= b[i];
Generic MIP optimization problem
Parameters
14. Generic MIP optimization problem
param m; param n; param l;
set I := 1 .. m;
set J := 1 .. n;
set K := 1 .. l;
param c{J}; param d{K};
param a{I, J}; param e{I, K}; param b{I};
var x{J} integer, >= 0;
var y{K} >= 0;
minimize f: sum{j in J} c[j] * x[j] + sum{k in K} d[k] * y[k];
subject to g{i in I}:
sum{j in J} a[i,j] * x[j] + sum{k in K} e[i,k] * y[k] <= b[i];
Indexing sets
16. Why MathProg in Scala
Generate constraints
Create derived models
Develop custom resolution methods at high
level
...
17. MathProg in Scala (deep embedding)
Syntax
Represent MathProg models with Scala data
types
Mimic MathProg syntax with Scala functions
Semantics
Generate MathProg code, others ...
18. MathProg in Scala (deep embedding)
Syntax
Represent MathProg models with Scala data
types
Mimic MathProg syntax with Scala functions
Semantics
Generate MathProg code, others ...
Abstract syntax tree
19. MathProg in Scala (deep embedding)
Syntax
Represent MathProg models with Scala data
types
Mimic MathProg syntax with Scala functions
Semantics
Generate MathProg code, others ...
Smart constructors and
combinators
Abstract syntax tree
20. MathProg EDSL
param m;
param n;
param l;
set I := 1 .. m;
set J := 1 .. n;
set K := 1 .. l;
param c{J};
param d{K};
param a{I, J};
// ...
var x{J} integer, >= 0;
var y{K} >= 0;
val m = param("m")
val n = param("n")
val l = param("l")
val I = set("I") := 1 to m
val J = set("J") := 1 to n
val K = set("K") := 1 to l
val c = param("c", J)
val d = param("d", K)
val a = param("a", I, J)
// ...
val x = xvar("x", J).integer >= 0
val y = xvar("y", K) >= 0
21. MathProg EDSL
param m;
param n;
param l;
set I := 1 .. m;
set J := 1 .. n;
set K := 1 .. l;
param c{J};
param d{K};
param a{I, J};
// ...
var x{J} integer, >= 0;
var y{K} >= 0;
val m = param("m")
val n = param("n")
val l = param("l")
val I = set("I") := 1 to m
val J = set("J") := 1 to n
val K = set("K") := 1 to l
val c = param("c", J)
val d = param("d", K)
val a = param("a", I, J)
// ...
val x = xvar("x", J).integer >= 0
val y = xvar("y", K) >= 0
set
param
xvar ...
22. MathProg EDSL
param m;
param n;
param l;
set I := 1 .. m;
set J := 1 .. n;
set K := 1 .. l;
param c{J};
param d{K};
param a{I, J};
// ...
var x{J} integer, >= 0;
var y{K} >= 0;
val m = param("m")
val n = param("n")
val l = param("l")
val I = set("I") := 1 to m
val J = set("J") := 1 to n
val K = set("K") := 1 to l
val c = param("c", J)
val d = param("d", K)
val a = param("a", I, J)
// ...
val x = xvar("x", J).integer >= 0
val y = xvar("y", K) >= 0
set
param
xvar ...
ParamStat(
"c",
domain = Some(
IndExpr(
List(
IndEntry(
Nil,
SetRef(J)
)
))))
23. val m = param("m")
val n = param("n")
val l = param("l")
val I = set("I") := 1 to m
val J = set("J") := 1 to n
val K = set("K") := 1 to l
val c = param("c", J)
val d = param("d", K)
val a = param("a", I, J)
// ...
val x = xvar("x", J).integer >= 0
val y = xvar("y", K) >= 0
MathProg EDSL
Scala range
notation for
arithmetic
sets
param m;
param n;
param l;
set I := 1 .. m;
set J := 1 .. n;
set K := 1 .. l;
param c{J};
param d{K};
param a{I, J};
// ...
var x{J} integer, >= 0;
var y{K} >= 0;
24. val m = param("m")
val n = param("n")
val l = param("l")
val I = set("I") := 1 to m
val J = set("J") := 1 to n
val K = set("K") := 1 to l
val c = param("c", J)
val d = param("d", K)
val a = param("a", I, J)
// ...
val x = xvar("x", J).integer >= 0
val y = xvar("y", K) >= 0
param m;
param n;
param l;
set I := 1 .. m;
set J := 1 .. n;
set K := 1 .. l;
param c{J};
param d{K};
param a{I, J};
// ...
var x{J} integer, >= 0;
var y{K} >= 0;
MathProg EDSL
Varargs for
simple indexing
expressions
25. val m = param("m")
val n = param("n")
val l = param("l")
val I = set("I") := 1 to m
val J = set("J") := 1 to n
val K = set("K") := 1 to l
val c = param("c", J)
val d = param("d", K)
val a = param("a", I, J)
// ...
val x = xvar("x", J).integer >= 0
val y = xvar("y", K) >= 0
param m;
param n;
param l;
set I := 1 .. m;
set J := 1 .. n;
set K := 1 .. l;
param c{J};
param d{K};
param a{I, J};
// ...
var x{J} integer, >= 0;
var y{K} >= 0;
MathProg EDSL
Returns a new
variable with the
`integer’ attribute
26. val m = param("m")
val n = param("n")
val l = param("l")
val I = set("I") := 1 to m
val J = set("J") := 1 to n
val K = set("K") := 1 to l
val c = param("c", J)
val d = param("d", K)
val a = param("a", I, J)
// ...
val x = xvar("x", J).integer >= 0
val y = xvar("y", K) >= 0
MathProg EDSL
Another variable,
now adding the
lower bound
attribute
param m;
param n;
param l;
set I := 1 .. m;
set J := 1 .. n;
set K := 1 .. l;
param c{J};
param d{K};
param a{I, J};
// ...
var x{J} integer, >= 0;
var y{K} >= 0;
27. val m = param("m")
val n = param("n")
val l = param("l")
val I = set("I") := 1 to m
val J = set("J") := 1 to n
val K = set("K") := 1 to l
val c = param("c", J)
val d = param("d", K)
val a = param("a", I, J)
// ...
val x = xvar("x", J).integer >= 0
val y = xvar("y", K) >= 0
param m;
param n;
param l;
set I := 1 .. m;
set J := 1 .. n;
set K := 1 .. l;
param c{J};
param d{K};
param a{I, J};
// ...
var x{J} integer, >= 0;
var y{K} >= 0;
MathProg EDSL
Everything is
immutable
28. MathProg EDSL
minimize f:
sum{j in J} c[j] * x[j] +
sum{k in K} d[k] * y[k];
s.t. g{i in I}:
sum{j in J} a[i,j] * x[j] +
sum{k in K} e[i,k] * y[k] <=
b[i];
val f =
minimize("f") {
sum(j in J)(c(j) * x(j)) +
sum(k in K)(d(k) * y(k))
}
val g =
st("g", i in I) {
sum(j in J)(a(i, j) * x(j)) +
sum(k in K)(e(i, k) * y(k)) <=
b(i)
}
29. MathProg EDSL minimize
st ...
minimize f:
sum{j in J} c[j] * x[j] +
sum{k in K} d[k] * y[k];
s.t. g{i in I}:
sum{j in J} a[i,j] * x[j] +
sum{k in K} e[i,k] * y[k] <=
b[i];
val f =
minimize("f") {
sum(j in J)(c(j) * x(j)) +
sum(k in K)(d(k) * y(k))
}
val g =
st("g", i in I) {
sum(j in J)(a(i, j) * x(j)) +
sum(k in K)(e(i, k) * y(k)) <=
b(i)
}
30. minimize f:
sum{j in J} c[j] * x[j] +
sum{k in K} d[k] * y[k];
s.t. g{i in I}:
sum{j in J} a[i,j] * x[j] +
sum{k in K} e[i,k] * y[k] <=
b[i];
val f =
minimize("f") {
sum(j in J)(c(j) * x(j)) +
sum(k in K)(d(k) * y(k))
}
val g =
st("g", i in I) {
sum(j in J)(a(i, j) * x(j)) +
sum(k in K)(e(i, k) * y(k)) <=
b(i)
}
MathProg EDSL Multiple
parameters lists
31. minimize f:
sum{j in J} c[j] * x[j] +
sum{k in K} d[k] * y[k];
s.t. g{i in I}:
sum{j in J} a[i,j] * x[j] +
sum{k in K} e[i,k] * y[k] <=
b[i];
val f =
minimize("f") {
sum(j in J)(c(j) * x(j)) +
sum(k in K)(d(k) * y(k))
}
val g =
st("g", i in I) {
sum(j in J)(a(i, j) * x(j)) +
sum(k in K)(e(i, k) * y(k)) <=
b(i)
}
MathProg EDSL Multiple
parameters lists
32. minimize f:
sum{j in J} c[j] * x[j] +
sum{k in K} d[k] * y[k];
s.t. g{i in I}:
sum{j in J} a[i,j] * x[j] +
sum{k in K} e[i,k] * y[k] <=
b[i];
val f =
minimize("f") {
sum(j in J)(c(j) * x(j)) +
sum(k in K)(d(k) * y(k))
}
val g =
st("g", i in I) {
sum(j in J)(a(i, j) * x(j)) +
sum(k in K)(e(i, k) * y(k)) <=
b(i)
}
MathProg EDSL addition,
multiplication,
summation ...
33. minimize f:
sum{j in J} c[j] * x[j] +
sum{k in K} d[k] * y[k];
s.t. g{i in I}:
sum{j in J} a[i,j] * x[j] +
sum{k in K} e[i,k] * y[k] <=
b[i];
val f =
minimize("f") {
sum(j in J)(c(j) * x(j)) +
sum(k in K)(d(k) * y(k))
}
val g =
st("g", i in I) {
sum(j in J)(a(i, j) * x(j)) +
sum(k in K)(e(i, k) * y(k)) <=
b(i)
}
MathProg EDSL References to
individual parameters
and variables by
application
34. minimize f:
sum{j in J} c[j] * x[j] +
sum{k in K} d[k] * y[k];
s.t. g{i in I}:
sum{j in J} a[i,j] * x[j] +
sum{k in K} e[i,k] * y[k] <=
b[i];
val f =
minimize("f") {
sum(j in J)(c(j) * x(j)) +
sum(k in K)(d(k) * y(k))
}
val g =
st("g", i in I) {
sum(j in J)(a(i, j) * x(j)) +
sum(k in K)(e(i, k) * y(k)) <=
b(i)
}
MathProg EDSL
Constraint
35. minimize f:
sum{j in J} c[j] * x[j] +
sum{k in K} d[k] * y[k];
s.t. g{i in I}:
sum{j in J} a[i,j] * x[j] +
sum{k in K} e[i,k] * y[k] <=
b[i];
val f =
minimize("f") {
sum(j in J)(c(j) * x(j)) +
sum(k in K)(d(k) * y(k))
}
val g =
st("g", i in I) {
sum(j in J)(a(i, j) * x(j)) +
sum(k in K)(e(i, k) * y(k)) <=
b(i)
}
MathProg EDSL
Creates a new
constraint with the
specified name and
indexing
37. Implementing the syntax
Many of the syntactic constructs require:
A broad (open?) number of overloadings
Be used with infix notation
38. Many of the syntactic constructs require:
A broad (open?) number of overloadings
Be used with infix notation
Implementing the syntax
type classes and
object-oriented forwarders
39. Many of the syntactic constructs require:
A broad (open?) number of overloadings
Be used with infix notation
Implementing the syntax
type classes and
object-oriented forwarders
implicits prioritization
43. Implementing the syntax: “>=”
param("p", J) >= 0
implicit conversion to
something that
provides the method
ParamStat doesn’t have a `>=’
method
ParamStat
45. Implementing the syntax: “>=”
implicit class GTESyntax[A](lhe: A) {
def >=[B, C](rhe: B)(implicit GTEOp: GTEOp[A,B,C]): C =
GTEOp.gte(lhe, rhe)
}
46. Implementing the syntax: “>=”
implicit class GTESyntax[A](lhe: A) {
def >=[B, C](rhe: B)(implicit GTEOp: GTEOp[A,B,C]): C =
GTEOp.gte(lhe, rhe)
}
Fully generic
47. Implementing the syntax: “>=”
implicit class GTESyntax[A](lhe: A) {
def >=[B, C](rhe: B)(implicit GTEOp: GTEOp[A,B,C]): C =
GTEOp.gte(lhe, rhe)
}
trait GTEOp[A,B,C] {
def gte(lhe: A, rhe: B): C
}
Type class
48. Implementing the syntax: “>=”
implicit class GTESyntax[A](lhe: A) {
def >=[B, C](rhe: B)(implicit GTEOp: GTEOp[A,B,C]): C =
GTEOp.gte(lhe, rhe)
}
trait GTEOp[A,B,C] {
def gte(lhe: A, rhe: B): C
}
Type class
The implementation is
forwarded to the implicit
instance
49. Implementing the syntax: “>=”
implicit class GTESyntax[A](lhe: A) {
def >=[B, C](rhe: B)(implicit GTEOp: GTEOp[A,B,C]): C =
GTEOp.gte(lhe, rhe)
}
The available implicit instances
encode the rules by which the
operators can be used
50. Implementing the syntax: “>=”
implicit class GTESyntax[A](lhe: A) {
def >=[B, C](rhe: B)(implicit GTEOp: GTEOp[A,B,C]): C =
GTEOp.gte(lhe, rhe)
}
The instance of the type class is
required at the method level, so
both A and B can be used in the
implicits search
51. Implementing the syntax: “>=”
instances
implicit def ParamStatGTEOp[A](
implicit conv: A => NumExpr):
GTEOp[ParamStat, A, ParamStat] = /* ... */
>= for parameters and “numbers”
param(“p”) >= m
52. Implementing the syntax: “>=”
instances
implicit def ParamStatGTEOp[A](
implicit conv: A => NumExpr):
GTEOp[ParamStat, A, ParamStat] = /* ... */
>= for parameters and “numbers”
param(“p”) >= m
53. Implementing the syntax: “>=”
instances
implicit def ParamStatGTEOp[A](
implicit conv: A => NumExpr):
GTEOp[ParamStat, A, ParamStat] = /* ... */
>= for parameters and “numbers”
param(“p”) >= m
View bound
54. Implementing the syntax: “>=”
instances
implicit def VarStatGTEOp[A](
implicit conv: A => NumExpr):
GTEOp[VarStat, A, VarStat] = /* ... */
>= for variables and “numbers”
xvar(“x”) >= m
Analogous to the
parameter case
55. Implementing the syntax: “>=”
instances
implicit def NumExprGTEOp[A, B](
implicit convA: A => NumExpr,
convB: B => NumExpr):
GTEOp[A, B, LogicExpr] = /* ... */
>= for two “numbers”
i >= j
56. Implementing the syntax: “>=”
instances
implicit def NumExprGTEOp[A, B](
implicit convA: A => NumExpr,
convB: B => NumExpr):
GTEOp[A, B, LogicExpr] = /* ... */
>= for two “numbers”
i >= j
57. Implementing the syntax: “>=”
instances
implicit def NumExprGTEOp[A, B](
implicit convA: A => NumExpr,
convB: B => NumExpr):
GTEOp[A, B, LogicExpr] = /* ... */
>= for two “numbers”
i >= j
58. Implementing the syntax: “>=”
instances
implicit def LinExprGTEOp[A, B](
implicit convA: A => LinExpr,
convB: B => LinExpr):
GTEOp[A, B, ConstraintStat] = /* ... */
>= for two “linear expressions”
x(i) >= 0 and 10 >= x(i)
Analogous to the
numerical
expression case
59. implicit def NumExprGTEOp[A, B](
implicit convA: A => NumExpr,
convB: B => NumExpr)
conflicts with:
implicit def ParamStatGTEOp[A](
implicit conv: A => NumExpr)
implicit def LinExprGTEOp[A, B](
implicit convA: A => LinExpr,
convB: B => LinExpr)
Instances are ambiguous
60. implicit def NumExprGTEOp[A, B](
implicit convA: A => NumExpr,
convB: B => NumExpr)
conflicts with:
implicit def ParamStatGTEOp[A](
implicit conv: A => NumExpr)
implicit def LinExprGTEOp[A, B](
implicit convA: A => LinExpr,
convB: B => LinExpr)
Instances are ambiguous
`ParamStat <% NumExpr‘
to allow
`param(“p2”) >= p1’
61. Instances are ambiguous
implicit def NumExprGTEOp[A, B](
implicit convA: A => NumExpr,
convB: B => NumExpr)
conflicts with:
implicit def ParamStatGTEOp[A](
implicit conv: A => NumExpr)
implicit def LinExprGTEOp[A, B](
implicit convA: A => LinExpr,
convB: B => LinExpr)
NumExpr <: LinExpr
=>
NumExpr <% LinExpr
64. Next steps
New semantics
More static controls
Support other kind of problems beyond MIP
Talk directly to solvers
Arity, scope, ...
Multi-stage stochastic programming
65. import amphip.dsl._import amphip.dsl._
amphip is a collection of
experiments around
manipulating MathProg
models within Scala
github.com/gerferra/amphip
amphip is a collection of
experiments around
manipulating MathProg
models within Scala
github.com/gerferra/amphip