.NET 8.0 LINQ bindings for the Z3 theorem prover from Microsoft Research.
Based on the proof of concept by Bart De Smet which was curated into Z3.LinqBinding by Ricardo Niepel.
A number of examples are included in this solution, which you can run from .NET Interactive (requires Visual Studio Code) or from Visual Studio.
Provide a solution where either X is true or Y is true, but not both (using a ValueTuple).
using (var ctx = new Z3Context())
{
var theorem = from t in ctx.NewTheorem<(bool x, bool y)>()
where t.x ^ t.y
select t;
var result = theorem.Solve();
Console.WriteLine(result);
}
Solve the following system with 3 variables, with linear equalities and inequalities:
$$ x_1 - x_2 \ge 1 \ x_1 - x_2 \le 3 \ x_1 = 2x_3 + x_2 $$
using (var ctx = new Z3Context())
{
var theorem = from t in ctx.NewTheorem<Symbols<int, int, int>>()
where t.X1 - t.X2 >= 1
where t.X1 - t.X2 <= 3
where t.X1 == (2 * t.X3) + t.X2
select t;
var result = theorem.Solve();
Console.WriteLine(result);
}
In this example, we have two countries that produce crude oil which we refine into three end-products: gasoline, jet fuel, and lubricant. The crude oil from each country yields different quantities of end-products once the oil is refined:
Saudi Arabia | Venezuela | |
---|---|---|
Cost | $20 / barrel | $15 / barrel |
Max Order | 9000 barrels | 6000 barrels |
Refining % | 30% gasolene | 40% gasolene |
40% jet fuel | 20% jet fuel | |
20% lubricant | 30% lubricant | |
10% waste | 10% waste |
Given we need to produce the following volume of refined end-product:
Product | Amount (barrels) |
---|---|
Gasolene | 1900 |
Jet Fuel | 1500 |
Lubricant | 500 |
What is the most cost efficient purchase strategy of crude oil from Saudi Arabia and Venezuela?
using (var ctx = new Z3Context())
{
var theorem = from t in ctx.NewTheorem<(double sa, double vz)>()
where 0.3 * t.sa + 0.4 * t.vz >= 1900 // Gasolene
where 0.4 * t.sa + 0.2 * t.vz >= 1500 // Jet fuel
where 0.2 * t.sa + 0.3 * t.vz >= 500 // Lubricant
where 0 <= t.sa && t.sa <= 9000 // Max # barrels we can purchase
where 0 <= t.vz && t.vz <= 6000 // Max # barrels we can purchase
orderby (20.0 * t.sa) + (15.0 * t.vz) // Optimize for cost
select t;
var result = theorem.Solve();
Console.WriteLine($"SA: {result.sa} barrels (${result.sa * 20}), VZ: {result.vz} barrels (${result.vz * 15})");
}
In this example, you want to minimize the cost of shipping goods from 2 different warehouses to 4 different customers. Each warehouse has a limited supply and each customer has a certain demand.
Cost of shipping ($ per product): | Customer 1 | Customer 2 | Customer 3 | Customer 4 | |
---|---|---|---|---|---|
Warehouse 1 | $1.00 | $3.00 | $0.50 | $4.00 | |
Warehouse 2 | $2.50 | $5.00 | $1.50 | $2.50 |
Number of products shipped: | Customer 1 | Customer 2 | Customer 3 | Customer 4 | Total shipped | Available | ||
---|---|---|---|---|---|---|---|---|
Warehouse 1 | 0 | 13,000 | 15,000 | 32,000 | 60,000 | <= | 60,000 | |
Warehouse 2 | 30,000 | 10,000 | 0 | 0 | 40,000 | <= | 80,000 | |
Total received | 30,000 | 23,000 | 15,000 | 32,000 | ||||
Ordered | 35,000 | 22,000 | 18,000 | 30,000 | ||||
Total Shipping Cost | $299,500.00 |
using (var ctx = new Z3Context())
{
var theorem =
from t in ctx.NewTheorem<(double w1c1, double w1c2, double w1c3, double w1c4, double w2c1, double w2c2, double w2c3, double w2c4)>()
where t.w1c1 + t.w1c2 + t.w1c3 + t.w1c4 <= 60_000 // Warehouse 1 Product Availability
where t.w2c1 + t.w2c2 + t.w2c3 + t.w2c4 <= 80_000 // Warehouse 2 Product Availability
where t.w1c1 + t.w2c1 == 35_000 && (t.w1c1 >= 0 && t.w2c1 >= 0) // Customer 1 Orders
where t.w1c2 + t.w2c2 == 22_000 && (t.w1c2 >= 0 && t.w2c2 >= 0) // Customer 2 Orders
where t.w1c3 + t.w2c3 == 18_000 && (t.w1c3 >= 0 && t.w2c3 >= 0) // Customer 3 Orders
where t.w1c4 + t.w2c4 == 30_000 && (t.w1c4 >= 0 && t.w2c4 >= 0) // Customer 4 Orders
orderby (1.00 * t.w1c1) + (3.00 * t.w1c2) + (0.50 * t.w1c3) + (4.00 * t.w1c4) +
(2.50 * t.w2c1) + (5.00 * t.w2c2) + (1.50 * t.w2c3) + (2.50 * t.w2c4) // Optimize for Total Shipping Cost
select t;
var result = theorem.Solve();
Console.WriteLine($"| | Customer 1 | Customer 2 | Customer 3 | Customer 4 |");
Console.WriteLine($"|---------------------|------------|-------------|------------|------------|");
Console.WriteLine($"| Warehouse 1 | {result.w1c1} | {result.w1c2} | {result.w1c3} | {result.w1c4} |");
Console.WriteLine($"| Warehouse 2 | {result.w2c1} | {result.w2c2} | {result.w2c3} | {result.w2c4} |");
Console.WriteLine();
Console.WriteLine(string.Create(CultureInfo.CreateSpecificCulture("en-US"), $"Total Cost: {1.00 * result.w1c1 + 3.00 * result.w1c2 + 0.50 * result.w1c3 + 4.00 * result.w1c4 + 2.50 * result.w2c1 + 5.00 * result.w2c2 + 1.50 * result.w2c3 + 2.50 * result.w2c4:C}"));
}
You can install the Z3.Linq NuGet Package.
Add the package:
#r "nuget:Z3.Linq"
Then add the following using statements:
using System;
using Z3.Linq;
Then you can copy any of the above samples.
Add the Z3.Linq
package.
Configure your application to target x64 platform. This is a requirement as Z3.Linq
uses the Microsoft.Z3 package.
There are a number of ways in which you could contribute to this project:
All PRs are welcome.
2009: Bart De Smet describes a prototype LINQ to Z3 binding in three blog posts:
2010: Bart was interviewed on Channel 9 about the LINQ to Z3 concept:
2012: Bart presented LINQ to Everything at TechEd Europe 2012:
2015: Z3 was open sourced under the MIT license and the source code was moved to GitHub, where it is actively maintained.
2015: Ricardo Niepel (Microsoft) publishes the sample as Z3.LinqBinding using .NET 4.5
and Z3 binaries 4.4.0
2018: Jean-Sylvain Boige (My Intelligence Agency) adds Missionaries And Cannibals sample.
2020: Karel Frajtak adds support for fractions.
2021: Howard van Rooijen and Ian Griffiths (endjin) upgrade the project to .NET 6.0
, added Optimize
support via LINQ's OrderBy
, ValueTuple support, demonstrate using record
types, and fix nullability issues. They upgraded the solution to use Z3 NuGet package, merged in features from Jean-Sylvain Boige and Karel Frajtak forks, created archives of Bart's original blog posts and talks. They republished the project as Z3.Linq, created a new Polyglot Notebook of samples, and published a NuGet package Z3.Linq.
2023: Whit Waldo upgrades the project to .NET 8.0
This project is sponsored by endjin, a UK based Technology Consultancy which specializes in Data, AI, DevOps & Cloud, and is a .NET Foundation Corporate Sponsor.
We help small teams achieve big things.
We produce two free weekly newsletters:
Keep up with everything that's going on at endjin via our blog, follow us on Twitter, YouTube or LinkedIn.
We have become the maintainers of a number of popular .NET Open Source Projects:
And we have over 50 Open Source projects of our own, spread across the following GitHub Orgs:
And the DevOps tooling we have created for managing all these projects is available on the PowerShell Gallery.
For more information about our products and services, or for commercial support of this project, please contact us.
This project has adopted a code of conduct adapted from the Contributor Covenant to clarify expected behavior in our community. This code of conduct has been adopted by many other projects. For more information see the Code of Conduct FAQ or contact hello@endjin.com with any additional questions or comments.
The IP Maturity Model is endjin's IP quality assessment framework, which we've developed over a number of years when doing due diligence assessments of 3rd party systems. We've codified the approach into a configurable set of rules, which are committed into the root of a repo, and a Azure Function HttpTrigger HTTP endpoint which can evaluate the ruleset, and render an svg badge for display in repo's readme.md
.