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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.
If you use NLPModels.jl in your work, please cite using the format given in CITATION.bib.
Optimization problems are represented by an instance of (a subtype of) AbstractNLPModel
. Such instances are composed of
NLPModelMeta
, which provides information about the problem, including the number of variables, constraints, bounds on the variables, etc.See the documentation for details on the models and the API.
pkg> add NLPModels
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
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 matrixIf 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)⋅vjtprod(model, x, u)
: evaluate the result of the matrix-vector product J(x)ᵀ⋅uThe 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)⋅vSeveral 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.
NLPModelMeta
objects have the following attributes (with S <: AbstractVector
):
Attribute | Type | Notes |
---|---|---|
nvar | Int | number of variables |
x0 | S | initial guess |
lvar | S | vector of lower bounds |
uvar | S | vector of upper bounds |
ifix | Vector{Int} | indices of fixed variables |
ilow | Vector{Int} | indices of variables with lower bound only |
iupp | Vector{Int} | indices of variables with upper bound only |
irng | Vector{Int} | indices of variables with lower and upper bound (range) |
ifree | Vector{Int} | indices of free variables |
iinf | Vector{Int} | indices of visibly infeasible bounds |
ncon | Int | total number of general constraints |
nlin | Int | number of linear constraints |
nnln | Int | number of nonlinear general constraints |
y0 | S | initial Lagrange multipliers |
lcon | S | vector of constraint lower bounds |
ucon | S | vector of constraint upper bounds |
lin | Vector{Int} | indices of linear constraints |
nln | Vector{Int} | indices of nonlinear constraints |
jfix | Vector{Int} | indices of equality constraints |
jlow | Vector{Int} | indices of constraints of the form c(x) ≥ cl |
jupp | Vector{Int} | indices of constraints of the form c(x) ≤ cu |
jrng | Vector{Int} | indices of constraints of the form cl ≤ c(x) ≤ cu |
jfree | Vector{Int} | indices of "free" constraints (there shouldn't be any) |
jinf | Vector{Int} | indices of the visibly infeasible constraints |
nnzo | Int | number of nonzeros in the gradient |
nnzj | Int | number of nonzeros in the sparse Jacobian |
nnzh | Int | number of nonzeros in the sparse Hessian |
minimize | Bool | true if optimize == minimize |
islp | Bool | true if the problem is a linear program |
name | String | problem name |
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
License: View license
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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.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition
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This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.
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This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.
#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management
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If you think that an entry-level data analyst role might be right for you, you might be wondering what to focus on in the first 90 days on the job. What skills should you have going in and what should you focus on developing in order to advance in this career path?
Let’s take a look at the most important things you need to know.
#data #data-analytics #data-science #data-analysis #big-data-analytics #data-privacy #data-structures #good-company
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For example, suppose you’re about to set off and design a program. In this case, it may be your integration platform project or your big warehouse project; however, the principles for designing that program of work is pretty much the same regardless of the actual details of the project.
#big data #bigdata #big data analytics #data management #data modeling #data governance #enterprise data #enterprise data management #edm
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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.
#big data #data #data analysis #data security #data integration #etl #data warehouse #data breach #elt