#### 100% online

Start instantly and learn at your own schedule.

#### Approx. 10 hours to complete

Suggested: 7 hours/week...

#### English

Subtitles: English

#### 100% online

Start instantly and learn at your own schedule.

#### Approx. 10 hours to complete

Suggested: 7 hours/week...

#### English

Subtitles: English

### Syllabus - What you will learn from this course

Week
1
1 hour to complete

## SAT/SMT basics, SAT examples

This module introduces SAT (satisfiability) and SMT (SAT modulo theories) from scratch, and gives a number of examples of how to apply SAT....
6 videos (Total 58 min), 2 readings, 3 quizzes
6 videos
Introduction to SAT7m
SMT syntax and tools11m
Eight queens problem9m
Binary Arithmetic: multiplication12m
Examples from the lecture10m
Eight queens formula in SMT syntax10m
3 practice exercises
Truth table2m
Binary multiplication2m
Week
2
17 hours to complete

## SMT applications

This module shows a number of applications of satisfiability modulo the theory of linear inequalities (SMT)...
4 videos (Total 33 min), 2 readings, 7 quizzes
4 videos
Solving Sudoku7m
Scheduling8m
Bounded model checking8m
Sudoku formula in SMT 2 format10m
Introduction10m
7 practice exercises
Rectangle fitting2m
Scheduling2m
Bounded Model Checking2m
Filling trucks for a magic factorys
A sudoku variants
Job schedulings
Program correctnesss
Week
3
1 hour to complete

## Theory and algorithms for CNF-based SAT

This module describes how a rule called Resolution serves to determine whether a propositional formula in conjunctive normal form (CNF) is unsatisfiable. It is shown how an approach called DPLL does the same job, and how it is related to resolution. Finally, it is shown how current SAT solvers essentially implement and optimize DPLL....
6 videos (Total 56 min), 5 quizzes
6 videos
Example of resolution8m
DPLL10m
Transforming DPLL to resolution9m
CDCL basics11m
CDCL optimizations6m
5 practice exercises
Resolution2m
apply resolution2m
DPLL2m
DPLL to resolution2m
CDCL basics
Week
4
1 hour to complete

## Theory and algorithms for SAT/SMT

This module consists of two parts. The first part is about transforming arbitrary propositional formulas to CNF, leading to the Tseitin transformation doing this job such that the size of the transformed formula is linear in the size of the original formula. The second part is about extending SAT to SMT, in particular to dealing with linear inequalities. It is shown how the Simplex method for linear optimization serves for this job; the Simplex method itself is explained in detail....
6 videos (Total 55 min), 4 quizzes
6 videos
The Tseitin transfomation10m
Introduction to the Simplex method7m
Optimizing by the Simplex method11m
Checking feasibility by the Simplex method8m
The Simplex method and SMT8m
4 practice exercises
Transforming a propositional formula to CNF
The Tseitin transfomation
Slack form
Optimizing by the Simplex method

## Instructor

### Hans Zantema

prof.dr.
Department of Mathematics and Computer Science