**Physical Design Notes**

LinkedIn profiles to evaluate
https://www.linkedin.com/in/patrick-spracklen-phd-27057217/

**Publications to evaluate**

1. Discriminating Instance Generation from Abstract Specifications: A Case Study with CP and MIPDiscriminating Instance Generation from Abstract Specifications: A Case Study with CP and MIP
International Conference on Integration of Constraint Programming, Artificial Intelligence, and Operations Research · Jan 1, 2020
1. Automatic Streamlining for Constrained OptimizationAutomatic Streamlining for Constrained Optimization
Conference on Principles and Practice of Constraint Programming · Jan 1, 2019: https://link.springer.com/chapter/10.1007/978-3-030-30048-7_22
1. A Framework for Constraint Based Local Search using EssenceA Framework for Constraint Based Local Search using Essence
International Joint Conference on Artificial Intelligence · Jan 1, 2018: https://www.ijcai.org/Proceedings/2018/173
1. Automatic Generation and Selection of Streamlined Constraint Models via Monte Carlo Search on a Model LatticeAutomatic Generation and Selection of Streamlined Constraint Models via Monte Carlo Search on a Model Lattice
Conference on Principles and Practice of Constraint Programming · Jan 1, 2018: https://link.springer.com/chapter/10.1007/978-3-319-98334-9_24
1. Automatically Improving Constraint Models in Savile RowAutomatically Improving Constraint Models in Savile Row
Artificial Intelligence 251 · Jul 1, 2017
1. Automatically Improving SAT Encoding of Constraint Problems through Common Subexpression Elimination in Savile Row.Automatically Improving SAT Encoding of Constraint Problems through Common Subexpression Elimination in Savile Row.
Springer International Publishing · Aug 13, 2015: https://link.springer.com/chapter/10.1007/978-3-319-23219-5_23

(these above 5-6 publications are attached to the linkedin profile above - how relevant??)

Others.....

https://arxiv.org/pdf/1509.01040.pdf
https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020GC008964
https://link.springer.com/article/10.1007/s10601-005-2239-9 (only able to retrieve short abstract)
https://link.springer.com/chapter/10.1007/978-3-642-21434-9_9
https://sites.cs.ucsb.edu/~bultan/publications/baki-dissertation.pdf
http://theory.stanford.edu/~barrett/pubs/LRT+16.pdf
http://www.diva-portal.org/smash/get/diva2:20454/FULLTEXT01.pdf

Questions
What aspects of these might be best related or relevant to JITX? How so?

Background / Terminology

*CONSTRAINT SATISFACTION*
Constraint Satisifaction is the process of finding a solution through a set of constraints that impose conditions that the variables must satisfy. A solution is therefore a set of values for the variables that satisfies all constraints—that is, a point in the feasible region.
-constraints on a finite domain
-search
-backtracking
-local search
Constraint propagation are other methods used on such problems; most of them are incomplete in general, that is, they may solve the problem or prove it unsatisfiable, but not always. Constraint propagation methods are also used in conjunction with search to make a given problem simpler to solve. Other considered kinds of constraints are on real or rational numbers; solving problems on these constraints is done via variable elimination or the simplex algorithm.
-constraint PLs - C++ or Java or Choco
-constraint logic programming
-constraint satisfaction toolkits
Problems that can be expressed as constraint satisfaction problems are the eight queens puzzle, the Sudoku solving problem and many other logic puzzles, the Boolean satisfiability problem, scheduling problems, bounded-error estimation problems and various problems on graphs such as the graph coloring problem. It can be shown that the arithmetic inequalities or equations present in some types of finite logic puzzles such as Futoshiki or Kakuro (cross sums)

Constraint satisfaction toolkits
Constraint satisfaction toolkits are software libraries for imperative programming languages that are used to encode and solve a constraint satisfaction problem.

Cassowary constraint solver, an open source project for constraint satisfaction (accessible from C, Java, Python and other languages).
**Comet**, a commercial programming language and toolkit
**Gecode**, an open source portable toolkit written in C++ developed as a production-quality and highly efficient implementation of a complete theoretical background.
**Gelisp**, an open source portable wrapper of Gecode to Lisp.
**IBM ILOG CP Optimizer**: C++, Python, Java, .NET libraries (proprietary, free for academic use).Successor of ILOG Solver/Scheduler, which was considered the market leader in commercial constraint programming software as of 2006
**JaCoP**, an open source Java constraint solver.
**OptaPlanner**, another open source Java constraint solver.
**Koalog**, a commercial Java-based constraint solver.
**logilab-constraint**, an open source constraint solver written in pure Python with constraint propagation algorithms.
**Minion**, an open-source constraint solver written in C++, with a small language for the purpose of specifying models/problems.
**ZDC**, an open source program developed in the Computer-Aided Constraint Satisfaction Project for modelling and solving constraint satisfaction problems.
*Note - Kaleidoscope is misleading. Wrong pathway.*

*BACKTRACKING*
Backtracking is a general algorithm for finding solutions to some computational problems, notably constraint satisfaction problems, that incrementally builds candidates to the solutions, and abandons a candidate ("backtracks") as soon as it determines that the candidate cannot possibly be completed to a valid solution. The classic textbook example of the use of backtracking is the eight queens puzzle, that asks for all arrangements of eight chess queens on a standard chessboard so that no queen attacks any other. In the common backtracking approach, the partial candidates are arrangements of k queens in the first k rows of the board, all in different rows and columns. Any partial solution that contains two mutually attacking queens can be abandoned. Backtracking is an important tool for solving constraint satisfaction problems,such as crosswords, verbal arithmetic, Sudoku, and many other puzzles. It is often the most convenient technique for parsing, for the knapsack problem and other combinatorial optimization problems.
-Tree structure
-Depth - First Order
Example: The backtracking algorithm traverses this search tree recursively, from the root down, in depth-first order.
-Reject Procedure uses boolean valued function
-Variable trail or timestamp

## also look up
Ariadne's thread (logic)
Backjumping
Backward chaining
Enumeration algorithm
Sudoku solving algorithms
Combinatorial optimization
Constraint logic programming
Concurrent constraint logic programming
Mathematical optimization
Heuristic algorithms
Nurse scheduling problem
Traveling tournament problem

Here's some internal clarification on the strengths needed:
They need excellent programming ability in implementing/inventing these tricky solvers.
They don't need perfect polish in software engineering in bringing a piece of code to production (Philippe-level is sufficient).
The bar is: they need to be able to single-handedly write a working algorithm at least. A generalist engineer can help them refactor/polish the code for production.
ML skills are a nice-to-have not must-have.
What is our current bottle-neck:
We are missing the implementation ability to get an already-designed algorithm running.
We haven't begun the work to bring a prototype implementation into production yet.