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# NLPModels.jl: Data Structures for Optimization Models

## NLPModels

This package provides general guidelines to represent non-linear programming (NLP) problems in Julia and a standardized API to evaluate the functions and their derivatives. The main objective is to be able to rely on that API when designing optimization solvers in Julia.

## How to Cite

If you use NLPModels.jl in your work, please cite using the format given in CITATION.bib.

## Optimization Problems

Optimization problems are represented by an instance of (a subtype of) `AbstractNLPModel`. Such instances are composed of

• an instance of `NLPModelMeta`, which provides information about the problem, including the number of variables, constraints, bounds on the variables, etc.
• other data specific to the provenance of the problem.

See the documentation for details on the models and the API.

## Installation

``````pkg> add NLPModels
``````

## Models

This package provides no models, although it allows the definition of manually written models.

Check the list of packages that define models in this page of the docs

## Main Methods

If `model` is an instance of an appropriate subtype of `AbstractNLPModel`, the following methods are normally defined:

• `obj(model, x)`: evaluate f(x), the objective at `x`
• `cons(model x)`: evaluate c(x), the vector of general constraints at `x`

The following methods are defined if first-order derivatives are available:

• `grad(model, x)`: evaluate ∇f(x), the objective gradient at `x`
• `jac(model, x)`: evaluate J(x), the Jacobian of c at `x` as a sparse matrix

If Jacobian-vector products can be computed more efficiently than by evaluating the Jacobian explicitly, the following methods may be implemented:

• `jprod(model, x, v)`: evaluate the result of the matrix-vector product J(x)⋅v
• `jtprod(model, x, u)`: evaluate the result of the matrix-vector product J(x)ᵀ⋅u

The following method is defined if second-order derivatives are available:

• `hess(model, x, y)`: evaluate ∇²L(x,y), the Hessian of the Lagrangian at `x` and `y`

If Hessian-vector products can be computed more efficiently than by evaluating the Hessian explicitly, the following method may be implemented:

• `hprod(model, x, v, y)`: evaluate the result of the matrix-vector product ∇²L(x,y)⋅v

Several in-place variants of the methods above may also be implemented.

The complete list of methods that an interface may implement can be found in the documentation.

## Attributes

`NLPModelMeta` objects have the following attributes (with `S <: AbstractVector`):

Bug reports and discussions

If you think you found a bug, feel free to open an issue. Focused suggestions and requests can also be opened as issues. Before opening a pull request, start an issue or a discussion on the topic, please.

If you want to ask a question not suited for a bug report, feel free to start a discussion here. This forum is for general discussion about this repository and the JuliaSmoothOptimizers, so questions about any of our packages are welcome.

Author: JuliaSmoothOptimizers
Source Code: https://github.com/JuliaSmoothOptimizers/NLPModels.jl

## Buddha Community

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If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

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## Getting Started With Data Lakes

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## 4 Tips To Become A Successful Entry-Level Data Analyst

Companies across every industry rely on big data to make strategic decisions about their business, which is why data analyst roles are constantly in demand. Even as we transition to more automated data collection systems, data analysts remain a crucial piece in the data puzzle. Not only do they build the systems that extract and organize data, but they also make sense of it –– identifying patterns, trends, and formulating actionable insights.

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## Enterprise Data Management: Stick to the Basics

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## How Has COVID-19 Impacted Data Science?

The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges.

Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.

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