Functions and Recursion. Dr. Philip Cannata 1

Size: px
Start display at page:

Download "Functions and Recursion. Dr. Philip Cannata 1"

Transcription

1 Functions and Recursion Dr. Philip Cannata 1

2 10 High Level Languages This Course Java (Object Oriented) Jython in Java Relation ASP RDF (Horn Clause Deduction, Semantic Web) Dr. Philip Cannata 2

3 let transformation, differed substitution and closures, and interpretation in FAE let transformation: (let ((A B)) C) == ((lambda (A) C) B) A B C A B (let (( x 3)) (let ((f (lambda (y) (+ x y)))) (f 4)) ((lambda (x) ((lambda (f) (f 4)) (lambda (y) (+ x y)))) 3) (app (fun 'x [app (fun 'f [app (id 'f) (num 4)]) (fun 'y (add (id 'x) (id 'y)))]) (num 3)) (app arg arg (app arg arg2 Differed substitution and closures: (asub 'f (closurev 'y (add (id 'x) (id 'y)) (asub 'x (numv 3) (mtsub))) (asub 'x (numv 3) (mtsub))) Interpretation: (interp (app (id 'f) (num 4)) (asub 'f (closurev 'y (add (id 'x) (id 'y)) (asub 'x (numv 3) (mtsub))) (asub 'x (numv 3) (mtsub)))) (numv 7) Dr. Philip Cannata 3 C

4 Eduardo Saenz (anon. to classmates) (1 day ago) - If our AST does not have let class nodes, then when our interpreter visits every node of the AST, making the environment of differed substitutions along the way, our environment will only have closures? You only do: (asub x 3 (mtsub)) when you encounter a let, and since our parser converts lets to lambdas we'll never see this type of differed substitution in our environment; only closures in our environment. Is this logic correct? Philip Cannata (Instructor) (Just now) - This is a good observation but try to understand the following three cases that can occur and see if you can distinguish when deferred substitution should be done in each of them and when function application should be done: > (parse '(with (f (fun (x) x)) 5)) (app (fun 'f (num 5)) (fun 'x (id 'x))) > (interp (parse '(with (f (fun (x) x)) 5)) (mtsub)) (numv 5) > (parse '(with (f (fun (x) x)) (f 5))) (app (fun 'f (app (id 'f) (num 5))) (fun 'x (id 'x))) > (interp (parse '(with (f (fun (x) x)) (f 5))) (mtsub)) (numv 5) > (parse '(with (f (fun (x) x)) ((fun (y) (+ y 2)) 5))) (app (fun 'f (app (fun 'y (add (id 'y) (num 2))) (num 5))) (fun 'x (id 'x))) > (interp (parse '(with (f (fun (x) x)) ((fun (y) (+ y 2)) 5))) (mtsub)) (numv 7) Dr. Philip Cannata 4

5 Static scoping: Static and Dynamic Scoping (interp (parse '{with {x 5 {f 4) (asub 'f (closurev 'y (add (id 'x) (id 'y)) (asub 'x (numv 3) (mtsub))) (asub 'x (numv 3) (mtsub)))) (numv 7) Dynamic Scoping: (interp (parse '{with {x 5 {f 4) (asub 'f (closurev 'y (add (id 'x) (id 'y)) (asub 'x (numv 3) (mtsub))) (asub 'x (numv 3) (mtsub)))) (numv 9) Think about this expression for both Static and Dynamic Scoping: (let ((z 3)) (let ((d 3) (f (lambda (x) x))) (let ((z 27)) (let ((z 3) (a 5) (x (lambda (x y) (+ x (+ y z))))) (let ((z 9) (a 7)) (x z a)))))) Dr. Philip Cannata 5

6 PLAI Chapters 4, 5 and 6 Chapter 6, Pages 41 & 42 first-order Functions are not values in the language. They can only be defined in a designated portion of the program, where they must be given names for use in the Page remainder 27 - " of the program. The functions in F1WAE are of this nature, which explains the 1 in the name of the language. higher-order Functions can return other functions as values. first-class Functions are values with all the rights of other values. In particular, they can be supplied as the value of arguments to functions, returned by functions as answers, and stored in data structures. Dr. Philip Cannata 6

7 $ java crono.crono (let ((z 17)) (let ((z 3) (a 5) (x (\ (x y) (- x (+ y z))))) (let ((z 10) (a 5)) (x z a)))) Evaluating: (let ((z 17)) (let ((z 3) (a 5) (x (\ (x y) (- x (+ y z))))) (let ((z 10) (a 5)) (x z a)))) empty Evaluating: (let ((z 3) (a 5) (x (\ (x y) (- x (+ y z))))) (let ((z 10) (a 5)) (x z a))) Evaluating: (\ (x y) (- x (+ y z))) Result: (\ (x y) (- x (+ y z))) [] Evaluating: (let ((z 10) (a 5)) (x z a)) z: 3 x: (\ (x y) (- x (+ y z))) [] Evaluating: (x z a) z: 10 x: (\ (x y) (- x (+ y z))) [] Evaluating: (- x (+ y z)) y: 5 x: 10 Evaluating: (+ y z) y: 5 x: 10 Result: 22 Result: -12 Result: -12 Result: -12 Result: -12 Result: -12 In Crono, \ mean lambda Dr. Philip Cannata 7

8 In CronoOptions.java set public static boolean ENVIRONMENT_DYNAMIC = true; Run ant to rebuild crono. $ java crono.crono (let ((z 17)) (let ((z 3) (a 5) (x (\ (x y) (- x (+ y z))))) (let ((z 10) (a 5)) (x z a)))) Evaluating: (let ((z 17)) (let ((z 3) (a 5) (x (\ (x y) (- x (+ y z))))) (let ((z 10) (a 5)) (x z a)))) empty Evaluating: (let ((z 3) (a 5) (x (\ (x y) (- x (+ y z))))) (let ((z 10) (a 5)) (x z a))) Evaluating: (\ (x y) (- x (+ y z))) Result: (\ (x y) (- x (+ y z))) [] Evaluating: (let ((z 10) (a 5)) (x z a)) z: 3 x: (\ (x y) (- x (+ y z))) [] Evaluating: (x z a) z: 10 x: (\ (x y) (- x (+ y z))) [] Evaluating: (- x (+ y z)) y: 5 z: 10 x: 10 Evaluating: (+ y z) y: 5 z: 10 x: 10 Result: 15 Result: -5 Result: -5 Result: -5 Result: -5 Result: -5 Dr. Philip Cannata 8

9 (let ((z 17)) (let ((z 3) (a 5) (x (\ (x y) (- x (+ y z))))) (let ((z 10) (a 5)) (x z (x 0 0))))) Evaluating: (let ((z 17)) (let ((z 3) (a 5) (x (\ (x y) (- x (+ y z))))) (let ((z 10) (a 5)) (x z (x 0 0))))) empty Evaluating: (let ((z 3) (a 5) (x (\ (x y) (- x (+ y z))))) (let ((z 10) (a 5)) (x z (x 0 0)))) Evaluating: (\ (x y) (- x (+ y z))) Result: (\ (x y) (- x (+ y z))) [] Evaluating: (let ((z 10) (a 5)) (x z (x 0 0))) z: 3 x: (\ (x y) (- x (+ y z))) [] Evaluating: (x z (x 0 0)) z: 10 x: (\ (x y) (- x (+ y z))) [] Evaluating: (x 0 0) z: 10 x: (\ (x y) (- x (+ y z))) [] Evaluating: (- x (+ y z)) y: 0 x: 0 Evaluating: (+ y z) y: 0 x: 0 Result: 17 Result: -17 Result: -17 Evaluating: (- x (+ y z)) y: -17 x: 10 Evaluating: (+ y z) y: -17 x: 10 Result: 0 Result: 10 Result: 10 Result: 10 Result: 10 Result: 10 Notice function application here. Dr. Philip Cannata 9

10 (let ((z 17)(i 22)) (let ((z 3) (a 5) (x (\ (x y) (- x (+ y z))))) (let ((z 10) (a 5)) (x z (x 0 ((\ (x) x) i)))))) Evaluating: (let ((z 17) (i 22)) (let ((z 3) (a 5) (x (\ (x y) (- x (+ y z))))) (let ((z 10) (a 5)) (x z (x 0 ((\ (x) x) i)))))) empty Evaluating: (let ((z 3) (a 5) (x (\ (x y) (- x (+ y z))))) (let ((z 10) (a 5)) (x z (x 0 ((\ (x) x) i))))) Evaluating: (\ (x y) (- x (+ y z))) Result: (\ (x y) (- x (+ y z))) [, ] Evaluating: (let ((z 10) (a 5)) (x z (x 0 ((\ (x) x) i)))) z: 3 x: (\ (x y) (- x (+ y z))) [, ] Evaluating: (x z (x 0 ((\ (x) x) i))) z: 10 x: (\ (x y) (- x (+ y z))) [, ] Evaluating: (x 0 ((\ (x) x) i)) z: 10 x: (\ (x y) (- x (+ y z))) [, ] Evaluating: ((\ (x) x) i) z: 10 x: (\ (x y) (- x (+ y z))) [, ] Evaluating: (\ (x) x) z: 10 x: (\ (x y) (- x (+ y z))) [, ] Result: (\ (x) x) [,, z: 10, x: (\ (x y) (- x (+ y z)))] Result: 22 Evaluating: (- x (+ y z)) y: 22 x: 0 Evaluating: (+ y z) y: 22 x: 0 Result: 39 Result: -39 Result: -39 Evaluating: (- x (+ y z)) y: -39 x: 10 Evaluating: (+ y z) y: -39 x: 10 Result: -22 Result: 32 Result: 32 Result: 32 Result: 32 Result: 32 Dr. Philip Cannata 10

11 10 High Level Languages This Course Java (Object Oriented) Jython in Java Relation ASP RDF (Horn Clause Deduction, Semantic Web) Dr. Philip Cannata 11

12 Definitions An argument is an expression that appears in a function application/ call. A parameter is an identifier that appears in a function definition/ declaration. In the code on the right the call A(a, b) has arguments a and b. The function declaration A has parameters x and y. Parameter and Arguments int h, i; void B(int w) { int j = 1, k = 2; i = 2*w; w = w+1; printf("in B - w, j, k, h, i: %d, %d, %d, %d, %d\n", w, j, k, h, i); void A(int x, int y) { float i = 1.1, j = 2.2; B(h); printf("in A - x, y, i, j, h: %d, %d, %f, %f, %d\n", x, y, i, j, h); int main() { int a, b; h = 5; a = 3; b = 2; A(a, b); printf("in Main a, b, h, i: %d, %d, %d, %d\n", a, b, h, i); Dr. Philip Cannata 12

13 By value - Compute the value of the argument at the time of the call and assign that value to the parameter. So passing by value doesn t normally allow the called function to modify an argument s value. By reference - Compute the address of the argument at the time of the call and assign it to the parameter. Passing by value allows the called function to modify an argument s value. By value-result By name Parameter Passing Mechanisms Dr. Philip Cannata 13

14 int h, i; void B(int w) { int j = 1, k = 2; i = 2*w; w = w+1; Pass by Value printf("in B - w, j, k, h, i: %d, %d, %d, %d, %d\n", w, j, k, h, i); void A(int x, int y) { float i = 1.1, j = 2.2; B(h); printf("in A - x, y, i, j, h: %d, %d, %f, %f, %d\n", x, y, i, j, h); int main() { int a, b; h = 5; a = 3; b = 2; A(a, b); printf("in Main a, b, h, i: %d, %d, %d, %d\n", a, b, h, i); int h, i; void B(int *w) { int j = 1, k = 2; i = 2*(*w); *w = *w + 1; Pass by Reference printf("in B - w, j, k, h, i: %d, %d, %d, %d, %d\n", w, j, k, h, i); void A(int *x, int *y) { float i = 1.1, j = 2.2; B(&h); printf("in A - x, y, i, j, h: %d, %d, %f, %f, %d\n", x, y, i, j, h); int main() { int a, b; h = 5; a = 3; b = 2; A(&a, &b ); printf("in Main a, b, h, i: %d, %d, %d, %d\n", a, b, h, i); $./a In B - w, j, k, h, i: 6, 1, 2, 5, 10 In A - x, y, i, j, h: 3, 2, , , 5 In Main a, b, h, i: 3, 2, 5, 10 $./a In B - w, j, k, h, i: , 1, 2, 6, 10 In A - x, y, i, j, h: , , , , 6 In Main a, b, h, i: 3, 2, 6, 10 Dr. Philip Cannata 14

15 Pass by Value-Results Pass by value at the time of the call and/or copy the result back to the argument at the end of the call also called copy-in-copy-out. Pass by Name Textually substitute the argument for every instance of its corresponding parameter in the function body. Originated with Algol 60 (Jensen s device), but was dropped by Algol s successors -- Pascal, Ada, Modula. real procedure Sum(j, lo, hi, Ej); x[j]*j value lo, hi; integer j, lo, hi; real Ej; begin real S; S := 0; for j := lo step 1 until hi do S := S + Ej; Sum := S end; Exemplifies late binding, since evaluation of the argument is delayed until its occurrence in the function body is actually executed. Associated with lazy evaluation in functional languages (e.g., Haskell). Dr. Philip Cannata 15

16 This can be thought of as a stack with cons s on it. (cons 1 (cons 2 (cons 3 (cons 4 (cons 5 (cons 5 '())))))) Recursive Functions Exemplified by foldr in lisp (letrec ((f (lambda (l) (if (null? l) '() (cons (car l) (f (cdr l))))))) (f '( ))) '( ) (letrec ((f (lambda (v l) (if (null? l) v (cons (car l) (f v (cdr l))))))) (f '() '( ))) '( ) (letrec ((f (lambda (f1 v l) (if (null? l) v (f1 (car l) (f f1 v (cdr l))))))) (f cons '() '( ))) '( ) f == foldr If f1 == cons, foldr is the identity function for a list. It s the same as (cons 1 (cons 2 (cons 3( cons 4 (cons 5 (cons 6 '())))))) Dr. Philip Cannata 16 (cons 1 (cons 2 (cons 3 (cons 4 (cons 5 (cons 5 '())))))) Here the stack is upside down

17 Recursive Functions Exemplified by foldl in lisp Nothing goes on the stack in this case. (letrec ((f (lambda (f1 v l) (if (null? l) v (f f1 (car l) (cdr l)))))) (f cons '() '( ))) 6 (letrec ((f (lambda (f1 v l) (if (null? l) v (f f1 (f1 (car l) v) (cdr l)))))) (f cons '() '( ))) '( ) f == foldl If f1 == cons, foldl reverses the list. foldl is tail-recursive because nothing goes on the stack. It s the same as (cons 6 (cons 5 (cons 4 ( cons 3 (cons 2 (cons 1 '())))))) Dr. Philip Cannata 17

18 Recursive Functions A function that can call itself, either directly or indirectly, is a recursive function. $ cat test.c int factorial (int n) { int i; if (n < 2) { printf("factorial returning 1\n"); return 1; else { i = n * factorial(n-1); printf("factorial returning %d\n", i); return i; int main() { printf("factorial(3) returns: %d\n", factorial(3)); $./a factorial returning 1 factorial returning 2 factorial returning 6 factorial(3) returns: 6 Dr. Philip Cannata 18

19 Runtime Stack A stack of activation records: An activation record is a block of information associated with each function call, which includes: parameters and local variables Return address Each new call pushes an activation record, and each completing call pops the topmost one. So, the topmost record is the most recent call, and the stack has all active calls at any run-time moment. Dr. Philip Cannata 19

20 Runtime Stack for Functions Program int h, i;! void B(int w) {! int j, k;! i = 2*w;! w = w+1;!! void A(int x, int y) {! bool i, j;! B(h);!! int main() {! int a, b;! h = 5; a = 3; b = 2;! A(a, b);!! parameters and local variables Return address Saved registers Temporary variables Return value Dr. Philip Cannata 20

21 Calling function: factorial BasePtr = 3, printing runtime stack n: 3 answer: null number: 3 Calling function: factorial BasePtr = 5, printing runtime stack n: 2 n: 3 answer: null number: 3 Calling function: factorial BasePtr = 7, printing runtime stack n: 1 n: 2 n: 3 answer: null number: 3 Calling function: factorial BasePtr = 9, printing runtime stack n: 0 n: 1 n: 2 n: 3 answer: null number: 3 Hmm Runtime Stack for Factorial 3 int factorial(int n) { if(n < 1) { return 1; else { return n * factorial(n - 1); int main() { int number, answer; number = 3; answer = factorial(number); print(answer); Exiting function: factorial BasePtr = 9, printing runtime stack n: 0 return#factorial: 1 n: 1 n: 2 n: 3 answer: null number: 3 Exiting function: factorial BasePtr = 7, printing runtime stack return#factorial: 1 n: 1 return#factorial: 1 n: 2 n: 3 answer: null number: 3 Exiting function: factorial BasePtr = 5, printing runtime stack return#factorial: 1 n: 2 return#factorial: 2 n: 3 answer: null number: 3 Exiting function: factorial BasePtr = 3, printing runtime stack return#factorial: 2 n: 3 return#factorial: 6 answer: null number: 3 Dr. Philip Cannata 21

Functions and Recursion

Functions and Recursion Programming Languages Functions and Recursion Dr. Philip Cannata 1 10 High Level Languages This Course Jython in Java Java (Object Oriented) ACL2 (Propositional Induction) Relation Algorithmic Information

More information

Reminder About Functions

Reminder About Functions Reminder About Functions (let ((z 17)) (let ((z 3) (a ) (x (lambda (x y) (- x (+ y z))))) (let ((z 0) (a )) (x z a)))) int h, i; void B(int w) { int j, k; i = 2*w; w = w+1; void A(int x, int y) { bool

More information

22c:111 Programming Language Concepts. Fall Functions

22c:111 Programming Language Concepts. Fall Functions 22c:111 Programming Language Concepts Fall 2008 Functions Copyright 2007-08, The McGraw-Hill Company and Cesare Tinelli. These notes were originally developed by Allen Tucker, Robert Noonan and modified

More information

known as non-void functions/methods in C/C++/Java called from within an expression.

known as non-void functions/methods in C/C++/Java called from within an expression. FUNCTIONS 1 OUTLINE Basic Terminology Function Call and Return Parameters Parameter Passing Mechanisms Activation Records Recursive Functions Run Time Stack Function Declaration and Call in Clite Completing

More information

Higher-Order Functions

Higher-Order Functions Higher-Order Functions 1 Why Functions as Values Abstraction is easier with functions as values abstract over add and sub cases filter, map, etc. What are objects? Callbacks? Separate deffun form becomes

More information

Names and Types. standard hue names. Dr. Philip Cannata 1

Names and Types. standard hue names. Dr. Philip Cannata 1 Names and Types standard hue names Dr. Philip Cannata 1 10 High Level Languages This Course Jython in Java Java (Object Oriented) ACL2 (Propositional Induction) Relation Algorithmic Information Theory

More information

CS 345. Functions. Vitaly Shmatikov. slide 1

CS 345. Functions. Vitaly Shmatikov. slide 1 CS 345 Functions Vitaly Shmatikov slide 1 Reading Assignment Mitchell, Chapter 7 C Reference Manual, Chapters 4 and 9 slide 2 Procedural Abstraction Can be overloaded (e.g., binary +) Procedure is a named

More information

Cost of Substitution

Cost of Substitution Cost of Substitution (interp {with {x 1} {with {y 2} {+ 100 {+ 99 {+ 98... {+ y x}}}}}} ) (interp {with {y 2} {+ 100 {+ 99 {+ 98... {+ y 1}}}}} ) (interp {+ 100 {+ 99 {+ 98... {+ 2 1}}}} ) With n variables,

More information

Fall 2012 CS345 Project Requirements and Suggestions (Version 4)

Fall 2012 CS345 Project Requirements and Suggestions (Version 4) Fall 2012 CS345 Project Requirements and Suggestions (Version 4) Languages developed or modified for your project should have the following properties unless there is a good reason not to (e.g., you re

More information

Name EID. (calc (parse '{+ {with {x {+ 5 5}} {with {y {- x 3}} {+ y y} } } z } ) )

Name EID. (calc (parse '{+ {with {x {+ 5 5}} {with {y {- x 3}} {+ y y} } } z } ) ) CS 345 Spring 2010 Midterm Exam Name EID 1. [4 Points] Circle the binding instances in the following expression: (calc (parse '+ with x + 5 5 with y - x 3 + y y z ) ) 2. [7 Points] Using the following

More information

Project 2: Scheme Interpreter

Project 2: Scheme Interpreter Project 2: Scheme Interpreter CSC 4101, Fall 2017 Due: 12 November 2017 For this project, you will implement a simple Scheme interpreter in C++ or Java. Your interpreter should be able to handle the same

More information

Implementing Recursion

Implementing Recursion Implementing Recursion Shriram Krishnamurthi 2003-09-19 We ve seen that there are (at least two, fairly distinct ways of representing environments To implement recursive environments, we need to provide

More information

Higher-Order Functions (Part I)

Higher-Order Functions (Part I) Higher-Order Functions (Part I) 1 Why Functions as Values Abstraction is easier with functions as values abstract over add and sub cases filter, map, etc. What are objects? Callbacks? Separate deffun form

More information

Tail Calls. CMSC 330: Organization of Programming Languages. Tail Recursion. Tail Recursion (cont d) Names and Binding. Tail Recursion (cont d)

Tail Calls. CMSC 330: Organization of Programming Languages. Tail Recursion. Tail Recursion (cont d) Names and Binding. Tail Recursion (cont d) CMSC 330: Organization of Programming Languages Tail Calls A tail call is a function call that is the last thing a function does before it returns let add x y = x + y let f z = add z z (* tail call *)

More information

Typed Recursion {with {mk-rec : (((num -> num) -> (num -> num)) -> (num -> num)) {fun {body : ((num -> num) -> (num -> num))} {{fun {fx :

Typed Recursion {with {mk-rec : (((num -> num) -> (num -> num)) -> (num -> num)) {fun {body : ((num -> num) -> (num -> num))} {{fun {fx : Recursion {with {mk-rec {fun {body} {{fun {fx} {fx fx}} {fun {fx} {{fun {f} {body f}} {fun {x} {{fx fx} x}}}}}}} {with {fib {mk-rec {fun {fib} {fun {n} {if0 n 1 {if0 {- n 1} 1 {+ {fib {- n 1}} {fib {-

More information

Name SOLUTIONS EID NOTICE: CHEATING ON THE MIDTERM WILL RESULT IN AN F FOR THE COURSE.

Name SOLUTIONS EID NOTICE: CHEATING ON THE MIDTERM WILL RESULT IN AN F FOR THE COURSE. CS 345 Fall TTh 2012 Midterm Exam B Name SOLUTIONS EID NOTICE: CHEATING ON THE MIDTERM WILL RESULT IN AN F FOR THE COURSE. 1. [5 Points] Give two of the following three definitions. [2 points extra credit

More information

Functional Programming - 2. Higher Order Functions

Functional Programming - 2. Higher Order Functions Functional Programming - 2 Higher Order Functions Map on a list Apply Reductions: foldr, foldl Lexical scoping with let s Functional-11, CS5314, Sp16 BGRyder 1 Higher Order Functions Functions as 1st class

More information

Functional Programming. Pure Functional Programming

Functional Programming. Pure Functional Programming Functional Programming Pure Functional Programming Computation is largely performed by applying functions to values. The value of an expression depends only on the values of its sub-expressions (if any).

More information

Principles of Programming Languages Topic: Scope and Memory Professor Louis Steinberg Fall 2004

Principles of Programming Languages Topic: Scope and Memory Professor Louis Steinberg Fall 2004 Principles of Programming Languages Topic: Scope and Memory Professor Louis Steinberg Fall 2004 CS 314, LS,BR,LTM: Scope and Memory 1 Review Functions as first-class objects What can you do with an integer?

More information

fjyswan Dr. Philip Cannata 1

fjyswan Dr. Philip Cannata 1 fjyswan Dr. Philip Cannata 1 10 High Level Languages This Course Jython in Java Java (Object Oriented) Relation ASP RDF (Horn Clause Deduction, Semantic Web) Dr. Philip Cannata 2 Dr. Philip Cannata 3 fjyswan

More information

CS 415 Midterm Exam Spring 2002

CS 415 Midterm Exam Spring 2002 CS 415 Midterm Exam Spring 2002 Name KEY Email Address Student ID # Pledge: This exam is closed note, closed book. Good Luck! Score Fortran Algol 60 Compilation Names, Bindings, Scope Functional Programming

More information

Every language has its own scoping rules. For example, what is the scope of variable j in this Java program?

Every language has its own scoping rules. For example, what is the scope of variable j in this Java program? Lexical Binding There are two ways a variable can be used in a program: As a declaration As a "reference" or use of the variable Scheme has two kinds of variable "declarations" -- the bindings of a let-expression

More information

Comp 311: Sample Midterm Examination

Comp 311: Sample Midterm Examination Comp 311: Sample Midterm Examination October 29, 2007 Name: Id #: Instructions 1. The examination is closed book. If you forget the name for a Scheme operation, make up a name for it and write a brief

More information

cs173: Programming Languages Midterm Exam

cs173: Programming Languages Midterm Exam cs173: Programming Languages Midterm Exam Fall 2002 Please read this page of instructions before you turn the page! This exam is worth 181 points. We will assign partial credit to partial responses, provided

More information

cs173: Programming Languages Final Exam

cs173: Programming Languages Final Exam cs173: Programming Languages Final Exam Fall 2002 Please read this page of instructions before you turn the page! This exam is worth 102 points. We will assign partial credit to partial responses, provided

More information

Principles of Programming Languages 2017W, Functional Programming

Principles of Programming Languages 2017W, Functional Programming Principles of Programming Languages 2017W, Functional Programming Assignment 3: Lisp Machine (16 points) Lisp is a language based on the lambda calculus with strict execution semantics and dynamic typing.

More information

MIDTERM EXAMINATION - CS130 - Spring 2005

MIDTERM EXAMINATION - CS130 - Spring 2005 MIDTERM EAMINATION - CS130 - Spring 2005 Your full name: Your UCSD ID number: This exam is closed book and closed notes Total number of points in this exam: 231 + 25 extra credit This exam counts for 25%

More information

Lecture 09: Data Abstraction ++ Parsing is the process of translating a sequence of characters (a string) into an abstract syntax tree.

Lecture 09: Data Abstraction ++ Parsing is the process of translating a sequence of characters (a string) into an abstract syntax tree. Lecture 09: Data Abstraction ++ Parsing Parsing is the process of translating a sequence of characters (a string) into an abstract syntax tree. program text Parser AST Processor Compilers (and some interpreters)

More information

CMSC 330: Organization of Programming Languages. Operational Semantics

CMSC 330: Organization of Programming Languages. Operational Semantics CMSC 330: Organization of Programming Languages Operational Semantics Notes about Project 4, Parts 1 & 2 Still due today (7/2) Will not be graded until 7/11 (along with Part 3) You are strongly encouraged

More information

COP4020 Programming Assignment 1 - Spring 2011

COP4020 Programming Assignment 1 - Spring 2011 COP4020 Programming Assignment 1 - Spring 2011 In this programming assignment we design and implement a small imperative programming language Micro-PL. To execute Mirco-PL code we translate the code to

More information

Discussion. Type 08/12/2016. Language and Type. Type Checking Subtypes Type and Polymorphism Inheritance and Polymorphism

Discussion. Type 08/12/2016. Language and Type. Type Checking Subtypes Type and Polymorphism Inheritance and Polymorphism Type Joseph Spring Discussion Languages and Type Type Checking Subtypes Type and Inheritance and 7COM1023 Programming Paradigms 1 2 Type Type denotes the kind of values that programs can manipulate: Simple

More information

CS 275 Name Final Exam Solutions December 16, 2016

CS 275 Name Final Exam Solutions December 16, 2016 CS 275 Name Final Exam Solutions December 16, 2016 You may assume that atom? is a primitive procedure; you don t need to define it. Other helper functions that aren t a standard part of Scheme you need

More information

Threads. {seqn {spawn EXPR 1 } {spawn EXPR 2 }} Runs EXPR 1 and EXPR 2 in any order, even interleaved with each other

Threads. {seqn {spawn EXPR 1 } {spawn EXPR 2 }} Runs EXPR 1 and EXPR 2 in any order, even interleaved with each other Sequential Programs So far, the language that we ve implemented is deterministic. Running a program multiple times (or computing things slightly more quickly or slowly) does not change the result of the

More information

Simple Lisp. Alonzo Church. John McCarthy. Turing. David Hilbert, Jules Richard, G. G. Berry, Georg Cantor, Bertrand Russell, Kurt Gödel, Alan

Simple Lisp. Alonzo Church. John McCarthy. Turing. David Hilbert, Jules Richard, G. G. Berry, Georg Cantor, Bertrand Russell, Kurt Gödel, Alan Alonzo Church John McCarthy Simple Lisp David Hilbert, Jules Richard, G. G. Berry, Georg Cantor, Bertrand Russell, Kurt Gödel, Alan Turing Dr. Philip Cannata 1 Simple Lisp See the class website for a pdf

More information

Running FAE Programs Natively

Running FAE Programs Natively Running FAE Programs Natively So far: explained various language constructs by using Scheme Explaining Scheme? Certain parts explained using simpler parts of Scheme Leftover parts explained by reduction

More information

State. Substitution relies on an identifer having a fxed value

State. Substitution relies on an identifer having a fxed value Part 1 1 State Substitution relies on an identifer having a fxed value {let {[x 5]} {let {[f {lambda {y} {+ x y}}]}... {f 1}}} = {let {[f {lambda {y} {+ 5 y}}]}... {f 1}} because x cannot change In Plait,

More information

The Compiler So Far. Lexical analysis Detects inputs with illegal tokens. Overview of Semantic Analysis

The Compiler So Far. Lexical analysis Detects inputs with illegal tokens. Overview of Semantic Analysis The Compiler So Far Overview of Semantic Analysis Adapted from Lectures by Profs. Alex Aiken and George Necula (UCB) Lexical analysis Detects inputs with illegal tokens Parsing Detects inputs with ill-formed

More information

Computer Science 21b (Spring Term, 2015) Structure and Interpretation of Computer Programs. Lexical addressing

Computer Science 21b (Spring Term, 2015) Structure and Interpretation of Computer Programs. Lexical addressing Computer Science 21b (Spring Term, 2015) Structure and Interpretation of Computer Programs Lexical addressing The difference between a interpreter and a compiler is really two points on a spectrum of possible

More information

Functional Programming. Big Picture. Design of Programming Languages

Functional Programming. Big Picture. Design of Programming Languages Functional Programming Big Picture What we ve learned so far: Imperative Programming Languages Variables, binding, scoping, reference environment, etc What s next: Functional Programming Languages Semantics

More information

Programming Languages. Dr. Philip Cannata 1

Programming Languages. Dr. Philip Cannata 1 Programming Languages Dr. Philip Cannata 1 10 High Level Languages This Course Jython in Java Java (Object Oriented) ACL2 (Propositional Induction) Relation Algorithmic Information Theory (Information

More information

Programming Languages

Programming Languages Programming Languages Tevfik Koşar Lecture - XIII March 2 nd, 2006 1 Roadmap Functional Languages Lambda Calculus Intro to Scheme Basics Functions Bindings Equality Testing Searching 2 1 Functional Languages

More information

COP4020 Programming Languages. Functional Programming Prof. Robert van Engelen

COP4020 Programming Languages. Functional Programming Prof. Robert van Engelen COP4020 Programming Languages Functional Programming Prof. Robert van Engelen Overview What is functional programming? Historical origins of functional programming Functional programming today Concepts

More information

Recursion & Iteration

Recursion & Iteration Recursion & Iteration York University Department of Computer Science and Engineering 1 Overview Recursion Examples Iteration Examples Iteration vs. Recursion Example [ref.: Chap 5,6 Wilensky] 2 Recursion

More information

CS450: Structure of Higher Level Languages Spring 2018 Assignment 7 Due: Wednesday, April 18, 2018

CS450: Structure of Higher Level Languages Spring 2018 Assignment 7 Due: Wednesday, April 18, 2018 CS450: Structure of Higher Level Languages Spring 2018 Assignment 7 Due: Wednesday, April 18, 2018 Taken from assignments by Profs. Carl Offner and Ethan Bolker Part 1 - Modifying The Metacircular Evaluator

More information

CSE413 Midterm. Question Max Points Total 100

CSE413 Midterm. Question Max Points Total 100 CSE413 Midterm 05 November 2007 Name Student ID Answer all questions; show your work. You may use: 1. The Scheme language definition. 2. One 8.5 * 11 piece of paper with handwritten notes Other items,

More information

Principles of Programming Languages

Principles of Programming Languages Principles of Programming Languages Lesson 14 Type Checking Collaboration and Management Dana Fisman www.cs.bgu.ac.il/~ppl172 1 Type Checking We return to the issue of type safety we discussed informally,

More information

CMSC 330: Organization of Programming Languages. Formal Semantics of a Prog. Lang. Specifying Syntax, Semantics

CMSC 330: Organization of Programming Languages. Formal Semantics of a Prog. Lang. Specifying Syntax, Semantics Recall Architecture of Compilers, Interpreters CMSC 330: Organization of Programming Languages Source Scanner Parser Static Analyzer Operational Semantics Intermediate Representation Front End Back End

More information

Types and Type Inference

Types and Type Inference CS 242 2012 Types and Type Inference Notes modified from John Mitchell and Kathleen Fisher Reading: Concepts in Programming Languages, Revised Chapter 6 - handout on Web!! Outline General discussion of

More information

Wellesley College CS251 Programming Languages Spring, 2000 FINAL EXAM REVIEW PROBLEM SOLUTIONS

Wellesley College CS251 Programming Languages Spring, 2000 FINAL EXAM REVIEW PROBLEM SOLUTIONS Wellesley College CS251 Programming Languages Spring, 2000 FINAL EXAM REVIEW PROBLEM SOLUTIONS This document contains solutions to the problems on the final exam review problems posted earlier except for

More information

CSE 413 Midterm, May 6, 2011 Sample Solution Page 1 of 8

CSE 413 Midterm, May 6, 2011 Sample Solution Page 1 of 8 Question 1. (12 points) For each of the following, what value is printed? (Assume that each group of statements is executed independently in a newly reset Scheme environment.) (a) (define x 1) (define

More information

The role of semantic analysis in a compiler

The role of semantic analysis in a compiler Semantic Analysis Outline The role of semantic analysis in a compiler A laundry list of tasks Scope Static vs. Dynamic scoping Implementation: symbol tables Types Static analyses that detect type errors

More information

A LISP Interpreter in ML

A LISP Interpreter in ML UNIVERSITY OF OSLO Department of Informatics A LISP Interpreter in ML Mandatory Assignment 1 INF3110 September 21, 2009 Contents 1 1 Introduction The purpose of this assignment is to write an interpreter,

More information

Semantic Analysis. Outline. The role of semantic analysis in a compiler. Scope. Types. Where we are. The Compiler so far

Semantic Analysis. Outline. The role of semantic analysis in a compiler. Scope. Types. Where we are. The Compiler so far Outline Semantic Analysis The role of semantic analysis in a compiler A laundry list of tasks Scope Static vs. Dynamic scoping Implementation: symbol tables Types Statically vs. Dynamically typed languages

More information

Scheme as implemented by Racket

Scheme as implemented by Racket Scheme as implemented by Racket (Simple view:) Racket is a version of Scheme. (Full view:) Racket is a platform for implementing and using many languages, and Scheme is one of those that come out of the

More information

Programming Languages. Dr. Philip Cannata 1

Programming Languages. Dr. Philip Cannata 1 Programming Languages Dr. Philip Cannata 0 High Level Languages This Course Java (Object Oriented) Jython in Java Relation ASP RDF (Horn Clause Deduction, Semantic Web) Dr. Philip Cannata Dr. Philip Cannata

More information

Functional Programming Languages (FPL)

Functional Programming Languages (FPL) Functional Programming Languages (FPL) 1. Definitions... 2 2. Applications... 2 3. Examples... 3 4. FPL Characteristics:... 3 5. Lambda calculus (LC)... 4 6. Functions in FPLs... 7 7. Modern functional

More information

Stack ADT. ! push(x) puts the element x on top of the stack! pop removes the topmost element from the stack.

Stack ADT. ! push(x) puts the element x on top of the stack! pop removes the topmost element from the stack. STACK Stack ADT 2 A stack is an abstract data type based on the list data model All operations are performed at one end of the list called the top of the stack (TOS) LIFO (for last-in first-out) list is

More information

High Level Languages. Java (Object Oriented) This Course. Jython in Java. Relation. ASP RDF (Horn Clause Deduction, Semantic Web) Dr.

High Level Languages. Java (Object Oriented) This Course. Jython in Java. Relation. ASP RDF (Horn Clause Deduction, Semantic Web) Dr. 10 High Level Languages This Course Java (Object Oriented) Jython in Java Relation ASP RDF (Horn Clause Deduction, Semantic Web) Dr. Philip Cannata 1 Dr. Philip Cannata 2 Programming Languages Lexical

More information

CSCI312 Principles of Programming Languages!

CSCI312 Principles of Programming Languages! CSCI312 Principles of Programming Languages! Scope Xu Liu ! 4.1 Syntactic Issues! 4.2 Variables! 4.3 Scope! 4.4 Symbol Table! 4.5 Resolving References! 4.6 Dynamic Scoping! 4.7 Visibility! 4.8 Overloading!

More information

CS 415 Midterm Exam Fall 2003

CS 415 Midterm Exam Fall 2003 CS 415 Midterm Exam Fall 2003 Name KEY Email Address Student ID # Pledge: This exam is closed note, closed book. Questions will be graded on quality of answer. Please supply the best answer you can to

More information

Design Issues. Subroutines and Control Abstraction. Subroutines and Control Abstraction. CSC 4101: Programming Languages 1. Textbook, Chapter 8

Design Issues. Subroutines and Control Abstraction. Subroutines and Control Abstraction. CSC 4101: Programming Languages 1. Textbook, Chapter 8 Subroutines and Control Abstraction Textbook, Chapter 8 1 Subroutines and Control Abstraction Mechanisms for process abstraction Single entry (except FORTRAN, PL/I) Caller is suspended Control returns

More information

LECTURE 18. Control Flow

LECTURE 18. Control Flow LECTURE 18 Control Flow CONTROL FLOW Sequencing: the execution of statements and evaluation of expressions is usually in the order in which they appear in a program text. Selection (or alternation): a

More information

An Introduction to Functions

An Introduction to Functions Chapter 4 An Introduction to Functions Through the agency of with, we have added identifiers and the ability to name expressions to the language. Much of the time, though, simply being able to name an

More information

Semantic Analysis. Outline. The role of semantic analysis in a compiler. Scope. Types. Where we are. The Compiler Front-End

Semantic Analysis. Outline. The role of semantic analysis in a compiler. Scope. Types. Where we are. The Compiler Front-End Outline Semantic Analysis The role of semantic analysis in a compiler A laundry list of tasks Scope Static vs. Dynamic scoping Implementation: symbol tables Types Static analyses that detect type errors

More information

Types, Type Inference and Unification

Types, Type Inference and Unification Types, Type Inference and Unification Mooly Sagiv Slides by Kathleen Fisher and John Mitchell Cornell CS 6110 Summary (Functional Programming) Lambda Calculus Basic ML Advanced ML: Modules, References,

More information

CS1622. Semantic Analysis. The Compiler So Far. Lecture 15 Semantic Analysis. How to build symbol tables How to use them to find

CS1622. Semantic Analysis. The Compiler So Far. Lecture 15 Semantic Analysis. How to build symbol tables How to use them to find CS1622 Lecture 15 Semantic Analysis CS 1622 Lecture 15 1 Semantic Analysis How to build symbol tables How to use them to find multiply-declared and undeclared variables. How to perform type checking CS

More information

CS4215 Programming Language Implementation

CS4215 Programming Language Implementation CS4215 Programming Language Implementation You have 45 minutes to complete the exam. Use a B2 pencil to fill up the provided MCQ form. Leave Section A blank. Fill up Sections B and C. After finishing,

More information

Organization of Programming Languages CS3200/5200N. Lecture 11

Organization of Programming Languages CS3200/5200N. Lecture 11 Organization of Programming Languages CS3200/5200N Razvan C. Bunescu School of Electrical Engineering and Computer Science bunescu@ohio.edu Functional vs. Imperative The design of the imperative languages

More information

Lecture 8: Recursion and Iteration. Exceptions. Declarative Programming.

Lecture 8: Recursion and Iteration. Exceptions. Declarative Programming. Lecture 8: Recursion and Iteration. Exceptions. Declarative Programming. September 26th, 2010 Lecture 8: Recursion and Iteration. Exceptions. Declarative Programming. (1/48) Lecture Outline Memory Management,

More information

NOTE: Answer ANY FOUR of the following 6 sections:

NOTE: Answer ANY FOUR of the following 6 sections: A-PDF MERGER DEMO Philadelphia University Lecturer: Dr. Nadia Y. Yousif Coordinator: Dr. Nadia Y. Yousif Internal Examiner: Dr. Raad Fadhel Examination Paper... Programming Languages Paradigms (750321)

More information

Box-and-arrow Diagrams

Box-and-arrow Diagrams Box-and-arrow Diagrams 1. Draw box-and-arrow diagrams for each of the following statements. What needs to be copied, and what can be referenced with a pointer? (define a ((squid octopus) jelly sandwich))

More information

Summer 2017 Discussion 10: July 25, Introduction. 2 Primitives and Define

Summer 2017 Discussion 10: July 25, Introduction. 2 Primitives and Define CS 6A Scheme Summer 207 Discussion 0: July 25, 207 Introduction In the next part of the course, we will be working with the Scheme programming language. In addition to learning how to write Scheme programs,

More information

Functional Programming. Pure Functional Languages

Functional Programming. Pure Functional Languages Functional Programming Pure functional PLs S-expressions cons, car, cdr Defining functions read-eval-print loop of Lisp interpreter Examples of recursive functions Shallow, deep Equality testing 1 Pure

More information

Functional Programming. Pure Functional Languages

Functional Programming. Pure Functional Languages Functional Programming Pure functional PLs S-expressions cons, car, cdr Defining functions read-eval-print loop of Lisp interpreter Examples of recursive functions Shallow, deep Equality testing 1 Pure

More information

Lecture 16: Static Semantics Overview 1

Lecture 16: Static Semantics Overview 1 Lecture 16: Static Semantics Overview 1 Lexical analysis Produces tokens Detects & eliminates illegal tokens Parsing Produces trees Detects & eliminates ill-formed parse trees Static semantic analysis

More information

Non-Functional Procedures

Non-Functional Procedures Functional Programs So far, the language that we ve implemented is purely functional A function produces the same result every time for the same arguments Real programming languages do not behave this

More information

CS 415 Midterm Exam Spring SOLUTION

CS 415 Midterm Exam Spring SOLUTION CS 415 Midterm Exam Spring 2005 - SOLUTION Name Email Address Student ID # Pledge: This exam is closed note, closed book. Questions will be graded on quality of answer. Please supply the best answer you

More information

;;; Determines if e is a primitive by looking it up in the primitive environment. ;;; Define indentation and output routines for the output for

;;; Determines if e is a primitive by looking it up in the primitive environment. ;;; Define indentation and output routines for the output for Page 1/11 (require (lib "trace")) Allow tracing to be turned on and off (define tracing #f) Define a string to hold the error messages created during syntax checking (define error msg ""); Used for fancy

More information

Functional Programming and Haskell

Functional Programming and Haskell Functional Programming and Haskell Tim Dawborn University of Sydney, Australia School of Information Technologies Tim Dawborn Functional Programming and Haskell 1/22 What are Programming Paradigms? A programming

More information

Functional Programming

Functional Programming Functional Programming CS331 Chapter 14 Functional Programming Original functional language is LISP LISt Processing The list is the fundamental data structure Developed by John McCarthy in the 60 s Used

More information

Parsing Scheme (+ (* 2 3) 1) * 1

Parsing Scheme (+ (* 2 3) 1) * 1 Parsing Scheme + (+ (* 2 3) 1) * 1 2 3 Compiling Scheme frame + frame halt * 1 3 2 3 2 refer 1 apply * refer apply + Compiling Scheme make-return START make-test make-close make-assign make- pair? yes

More information

CS 314 Principles of Programming Languages

CS 314 Principles of Programming Languages CS 314 Principles of Programming Languages Lecture 18: Functional Programming Zheng (Eddy) Zhang Rutgers University April 9, 2018 Review: Defining Scheme Functions (define ( lambda (

More information

Implementing Subprograms

Implementing Subprograms 1 Implementing Subprograms CS 315 Programming Languages Pinar Duygulu Bilkent University CS315 Programming Languages Pinar Duygulu The General Semantics of Calls and Returns 2 The subprogram call and return

More information

Chapter 15. Functional Programming Languages

Chapter 15. Functional Programming Languages Chapter 15 Functional Programming Languages Copyright 2009 Addison-Wesley. All rights reserved. 1-2 Chapter 15 Topics Introduction Mathematical Functions Fundamentals of Functional Programming Languages

More information

CS558 Programming Languages

CS558 Programming Languages CS558 Programming Languages Winter 2017 Lecture 7b Andrew Tolmach Portland State University 1994-2017 Values and Types We divide the universe of values according to types A type is a set of values and

More information

Logic - CM0845 Introduction to Haskell

Logic - CM0845 Introduction to Haskell Logic - CM0845 Introduction to Haskell Diego Alejandro Montoya-Zapata EAFIT University Semester 2016-1 Diego Alejandro Montoya-Zapata (EAFIT University) Logic - CM0845 Introduction to Haskell Semester

More information

Functional Languages. Hwansoo Han

Functional Languages. Hwansoo Han Functional Languages Hwansoo Han Historical Origins Imperative and functional models Alan Turing, Alonzo Church, Stephen Kleene, Emil Post, etc. ~1930s Different formalizations of the notion of an algorithm

More information

Midterm 2 Solutions Many acceptable answers; one was the following: (defparameter g1

Midterm 2 Solutions Many acceptable answers; one was the following: (defparameter g1 Midterm 2 Solutions 1. [20 points] Consider the language that consist of possibly empty lists of the identifier x enclosed by parentheses and separated by commas. The language includes { () (x) (x,x) (x,x,x)

More information

The results for a few specific cases below are indicated. allequal ([1,1,1,1]) should return true allequal ([1,1,2,1]) should return false

The results for a few specific cases below are indicated. allequal ([1,1,1,1]) should return true allequal ([1,1,2,1]) should return false Test 1 Multiple Choice. Write your answer to the LEFT of each problem. 4 points each 1. Which celebrity has not received an ACM Turing Award? A. Alan Kay B. John McCarthy C. Dennis Ritchie D. Bjarne Stroustrup

More information

Scheme Quick Reference

Scheme Quick Reference Scheme Quick Reference COSC 18 Winter 2003 February 10, 2003 1 Introduction This document is a quick reference guide to common features of the Scheme language. It is by no means intended to be a complete

More information

UNIVERSITY OF TORONTO Faculty of Arts and Science. Midterm Sample Solutions CSC324H1 Duration: 50 minutes Instructor(s): David Liu.

UNIVERSITY OF TORONTO Faculty of Arts and Science. Midterm Sample Solutions CSC324H1 Duration: 50 minutes Instructor(s): David Liu. UNIVERSITY OF TORONTO Faculty of Arts and Science Midterm Sample s CSC324H1 Duration: 50 minutes Instructor(s): David Liu. No Aids Allowed Name: Student Number: Please read the following guidelines carefully.

More information

Types and Type Inference

Types and Type Inference Types and Type Inference Mooly Sagiv Slides by Kathleen Fisher and John Mitchell Reading: Concepts in Programming Languages, Revised Chapter 6 - handout on the course homepage Outline General discussion

More information

LECTURE 16. Functional Programming

LECTURE 16. Functional Programming LECTURE 16 Functional Programming WHAT IS FUNCTIONAL PROGRAMMING? Functional programming defines the outputs of a program as a mathematical function of the inputs. Functional programming is a declarative

More information

Anatomy of a Compiler. Overview of Semantic Analysis. The Compiler So Far. Why a Separate Semantic Analysis?

Anatomy of a Compiler. Overview of Semantic Analysis. The Compiler So Far. Why a Separate Semantic Analysis? Anatomy of a Compiler Program (character stream) Lexical Analyzer (Scanner) Syntax Analyzer (Parser) Semantic Analysis Parse Tree Intermediate Code Generator Intermediate Code Optimizer Code Generator

More information

CPSC 311, 2010W1 Midterm Exam #2

CPSC 311, 2010W1 Midterm Exam #2 CPSC 311, 2010W1 Midterm Exam #2 2010/11/02 Page 1 of 18 CPSC 311, 2010W1 Midterm Exam #2 Name: Q1: 20 Student ID: Q2: 20 Signature (required; indicates agreement with rules below): Q3: 20 Q4: 20 Q5: 20

More information

Programming Languages. Dr. Philip Cannata 1

Programming Languages. Dr. Philip Cannata 1 Programming Languages Dr. Philip Cannata 0 High Level Languages This Course Jython in Java Java (Object Oriented) ACL (Propositional Induction) Relation Algorithmic Information Theory (Information Compression

More information

Lecture 15 CIS 341: COMPILERS

Lecture 15 CIS 341: COMPILERS Lecture 15 CIS 341: COMPILERS Announcements HW4: OAT v. 1.0 Parsing & basic code generation Due: March 28 th No lecture on Thursday, March 22 Dr. Z will be away Zdancewic CIS 341: Compilers 2 Adding Integers

More information

CPS 506 Comparative Programming Languages. Programming Language Paradigm

CPS 506 Comparative Programming Languages. Programming Language Paradigm CPS 506 Comparative Programming Languages Functional Programming Language Paradigm Topics Introduction Mathematical Functions Fundamentals of Functional Programming Languages The First Functional Programming

More information

CS 11 Haskell track: lecture 1

CS 11 Haskell track: lecture 1 CS 11 Haskell track: lecture 1 This week: Introduction/motivation/pep talk Basics of Haskell Prerequisite Knowledge of basic functional programming e.g. Scheme, Ocaml, Erlang CS 1, CS 4 "permission of

More information

CSSE 304 Assignment #13 (interpreter milestone #1) Updated for Fall, 2018

CSSE 304 Assignment #13 (interpreter milestone #1) Updated for Fall, 2018 CSSE 304 Assignment #13 (interpreter milestone #1) Updated for Fall, 2018 Deliverables: Your code (submit to PLC server). A13 participation survey (on Moodle, by the day after the A13 due date). This is

More information