https://mklab.gr/ Sun, 13 Sep 2026 01:33:42 +0000 MyBB https://mklab.gr/showthread.php?tid=1951 Sat, 12 Sep 2026 19:28:12 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=1951 Scientific Computing is a set of introductory lecture notes designed for mathematics students with no previous background in numerical analysis, combining numerical mathematics, MATLAB programming, dynamical systems, and computational physics. The book begins with core numerical methods—including IEEE floating-point arithmetic and round-off error, root-finding methods such as bisection and Newton’s method, numerical integration, Runge–Kutta methods for differential equations, Gaussian elimination and LU decomposition, finite differences, iterative methods, interpolation, and least-squares approximation.

 It then applies these techniques to nonlinear dynamical systems and chaos, using the pendulum to introduce stability, bifurcations, phase portraits, limit cycles, Poincaré sections, fractals, period doubling, and the Feigenbaum constant. The final section develops computational fluid dynamics, deriving the continuity and Navier–Stokes equations and showing how numerical methods can model laminar flow, vorticity, and two-dimensional flow past rectangular and circular obstacles. Overall, it is a practical bridge between mathematical theory and computer-based experimentation, with a strong emphasis on learning numerical methods through concrete scientific applications. 

LECTURE NOTES [PDF]]]> Scientific Computing is a set of introductory lecture notes designed for mathematics students with no previous background in numerical analysis, combining numerical mathematics, MATLAB programming, dynamical systems, and computational physics. The book begins with core numerical methods—including IEEE floating-point arithmetic and round-off error, root-finding methods such as bisection and Newton’s method, numerical integration, Runge–Kutta methods for differential equations, Gaussian elimination and LU decomposition, finite differences, iterative methods, interpolation, and least-squares approximation.

 It then applies these techniques to nonlinear dynamical systems and chaos, using the pendulum to introduce stability, bifurcations, phase portraits, limit cycles, Poincaré sections, fractals, period doubling, and the Feigenbaum constant. The final section develops computational fluid dynamics, deriving the continuity and Navier–Stokes equations and showing how numerical methods can model laminar flow, vorticity, and two-dimensional flow past rectangular and circular obstacles. Overall, it is a practical bridge between mathematical theory and computer-based experimentation, with a strong emphasis on learning numerical methods through concrete scientific applications. 

LECTURE NOTES [PDF]]]> https://mklab.gr/showthread.php?tid=1814 Fri, 04 Sep 2026 00:58:22 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=1814 Book name:The Nature of Computation
Authors: Cristopher Moore & Stephan Mertens
Publication date: 11 August 2011
Publisher: Oxford University Press
ISBN: 978-0-19-923321-2 (OUP Academic)

Summary
The Nature of Computation is a broad and unusually accessible introduction to theoretical computer science and computational complexity, written from a strongly mathematical perspective. Moore and Mertens aim to explain not merely how algorithms work, but what computation itself can and cannot accomplish. A central theme is the distinction between problems that can be solved efficiently and those for which finding a solution appears intrinsically difficult. This naturally leads to the classes P and NP, NP-completeness, reductions, and the famous unresolved question $P=NP?$ The authors deliberately minimize unnecessary formalism while retaining the mathematical ideas behind the theory. 

The book begins with fundamental algorithms and techniques such as recursion, divide-and-conquer, dynamic programming, greedy algorithms, shortest paths, and reductions. It then moves into increasingly sophisticated areas: NP-completeness, optimization and approximation algorithms, randomized computation, interactive proofs, pseudorandomness, random walks and Markov chains. A distinctive feature is the connection between computation and statistical physics: the authors examine counting and sampling problems and show how concepts such as phase transitions can arise in difficult computational problems. 

The final portions extend the discussion to the limits and alternative models of computation, most notably quantum computing. Rather than treating complexity theory as an isolated branch of computer science, Moore and Mertens connect it with mathematics, physics, biology, networks, probability, and optimization. The result is a substantial book—containing roughly 900 problems and exercises and 370 figures—that can function both as an advanced textbook and as a conceptual tour of modern complexity theory. It is particularly suitable for mathematically mature undergraduate or graduate students, physicists and mathematicians interested in computation, and computer scientists wanting a deeper conceptual view of their field. 

Key takeaways

• Computational complexity asks not only whether a problem can be solved, but how many computational resources are required to solve it.
  
• $P$ vs. $NP$ forms the conceptual heart of the book, with NP-completeness and reductions explaining why apparently unrelated difficult problems are deeply connected.
  
• The book goes well beyond classical complexity theory, covering randomized algorithms, interactive proofs, pseudorandomness, approximation, Markov chains and quantum computation.
  
• One of its most interesting features is the link between computation and statistical physics, particularly the appearance of phase-transition-like phenomena in NP-complete problems.
  
• Despite its depth, the authors intentionally emphasize intuition and explanation over excessive formalism, making difficult ideas more approachable than in many standard complexity-theory texts.
  
  

BOOK]]> Book name:The Nature of Computation
Authors: Cristopher Moore & Stephan Mertens
Publication date: 11 August 2011
Publisher: Oxford University Press
ISBN: 978-0-19-923321-2 (OUP Academic)

Summary
The Nature of Computation is a broad and unusually accessible introduction to theoretical computer science and computational complexity, written from a strongly mathematical perspective. Moore and Mertens aim to explain not merely how algorithms work, but what computation itself can and cannot accomplish. A central theme is the distinction between problems that can be solved efficiently and those for which finding a solution appears intrinsically difficult. This naturally leads to the classes P and NP, NP-completeness, reductions, and the famous unresolved question $P=NP?$ The authors deliberately minimize unnecessary formalism while retaining the mathematical ideas behind the theory. 

The book begins with fundamental algorithms and techniques such as recursion, divide-and-conquer, dynamic programming, greedy algorithms, shortest paths, and reductions. It then moves into increasingly sophisticated areas: NP-completeness, optimization and approximation algorithms, randomized computation, interactive proofs, pseudorandomness, random walks and Markov chains. A distinctive feature is the connection between computation and statistical physics: the authors examine counting and sampling problems and show how concepts such as phase transitions can arise in difficult computational problems. 

The final portions extend the discussion to the limits and alternative models of computation, most notably quantum computing. Rather than treating complexity theory as an isolated branch of computer science, Moore and Mertens connect it with mathematics, physics, biology, networks, probability, and optimization. The result is a substantial book—containing roughly 900 problems and exercises and 370 figures—that can function both as an advanced textbook and as a conceptual tour of modern complexity theory. It is particularly suitable for mathematically mature undergraduate or graduate students, physicists and mathematicians interested in computation, and computer scientists wanting a deeper conceptual view of their field. 

Key takeaways

• Computational complexity asks not only whether a problem can be solved, but how many computational resources are required to solve it.
  
• $P$ vs. $NP$ forms the conceptual heart of the book, with NP-completeness and reductions explaining why apparently unrelated difficult problems are deeply connected.
  
• The book goes well beyond classical complexity theory, covering randomized algorithms, interactive proofs, pseudorandomness, approximation, Markov chains and quantum computation.
  
• One of its most interesting features is the link between computation and statistical physics, particularly the appearance of phase-transition-like phenomena in NP-complete problems.
  
• Despite its depth, the authors intentionally emphasize intuition and explanation over excessive formalism, making difficult ideas more approachable than in many standard complexity-theory texts.
  
  

BOOK]]> https://mklab.gr/showthread.php?tid=1592 Sat, 15 Aug 2026 14:26:42 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=1592 Competitive Programmer’s Handbook
Author: Antti Laaksonen
Publication date: July 2018 draft
Publisher: Self-published / CSES, University of Helsinki
Length: 296 pages
Subject: Algorithms, data structures, competitive programming
Language: C++

REVIEW

Competitive Programmer’s Handbook is a practical and unusually broad introduction to algorithmic problem solving, written primarily for students preparing for programming competitions such as the International Olympiad in Informatics (IOI) and the International Collegiate Programming Contest (ICPC). Laaksonen assumes only basic programming knowledge and builds systematically from C++ techniques and computational complexity to sorting, binary search, data structures, complete search, greedy methods and dynamic programming. Rather than treating algorithms as isolated theoretical objects, the book repeatedly connects them to concrete problems and shows how the choice of algorithm determines whether a program can process its input within realistic time limits.

A major strength is the progression into graph algorithms. The reader encounters depth-first and breadth-first search, shortest paths using Bellman–Ford, Dijkstra and Floyd–Warshall, spanning trees, union-find, topological sorting, strongly connected components, lowest common ancestors, Eulerian and Hamiltonian paths, and network flows. The later sections move into more advanced competitive-programming material, including range queries, segment and binary indexed trees, bit manipulation, computational geometry, number theory, combinatorics, matrices, probability, string algorithms and optimization techniques. This breadth makes the handbook useful not just for competitions but also as a compact reference for a university algorithms course.

The book's greatest virtue is its emphasis on developing algorithmic intuition. It teaches the reader to ask: What are the constraints? What complexity is feasible? Which data structure represents the problem naturally? Can the problem be transformed into one I already know how to solve? Its concise explanations and numerous C++ examples make it especially suitable for learning by simultaneously solving problems on CSES or similar platforms. Readers seeking rigorous proofs comparable to a theoretical algorithms textbook may find some discussions brief, but that is deliberate: the goal is to turn mathematical and algorithmic ideas into working solutions. Laaksonen later developed closely related material into the Springer textbook Guide to Competitive Programming, which similarly presents competitive programming as a way of learning algorithms and computational thinking. 

Key takeaways

• Algorithmic efficiency is central: understanding complexities such as $O(n)$, $O(n\log n)$ and $O(n^2)$ helps determine whether a solution is practical.
  
• A relatively small toolkit solves many problems: dynamic programming, greedy algorithms, binary search, graph traversal and appropriate data structures recur throughout competitive programming.
  
• Mathematics and programming reinforce each other: number theory, combinatorics, probability and geometry become practical tools for constructing algorithms.
  
• The book is best used actively: reading a technique and then implementing it on actual programming problems is much more valuable than reading the handbook passively.
  

Overall: ★★★★★ — An excellent free introduction and reference for anyone who wants to learn algorithms through problem solving, particularly students interested in competitive programming, informatics olympiads, or strengthening their general algorithmic skills.


BOOK [PDF]]]> Competitive Programmer’s Handbook
Author: Antti Laaksonen
Publication date: July 2018 draft
Publisher: Self-published / CSES, University of Helsinki
Length: 296 pages
Subject: Algorithms, data structures, competitive programming
Language: C++

REVIEW

Competitive Programmer’s Handbook is a practical and unusually broad introduction to algorithmic problem solving, written primarily for students preparing for programming competitions such as the International Olympiad in Informatics (IOI) and the International Collegiate Programming Contest (ICPC). Laaksonen assumes only basic programming knowledge and builds systematically from C++ techniques and computational complexity to sorting, binary search, data structures, complete search, greedy methods and dynamic programming. Rather than treating algorithms as isolated theoretical objects, the book repeatedly connects them to concrete problems and shows how the choice of algorithm determines whether a program can process its input within realistic time limits.

A major strength is the progression into graph algorithms. The reader encounters depth-first and breadth-first search, shortest paths using Bellman–Ford, Dijkstra and Floyd–Warshall, spanning trees, union-find, topological sorting, strongly connected components, lowest common ancestors, Eulerian and Hamiltonian paths, and network flows. The later sections move into more advanced competitive-programming material, including range queries, segment and binary indexed trees, bit manipulation, computational geometry, number theory, combinatorics, matrices, probability, string algorithms and optimization techniques. This breadth makes the handbook useful not just for competitions but also as a compact reference for a university algorithms course.

The book's greatest virtue is its emphasis on developing algorithmic intuition. It teaches the reader to ask: What are the constraints? What complexity is feasible? Which data structure represents the problem naturally? Can the problem be transformed into one I already know how to solve? Its concise explanations and numerous C++ examples make it especially suitable for learning by simultaneously solving problems on CSES or similar platforms. Readers seeking rigorous proofs comparable to a theoretical algorithms textbook may find some discussions brief, but that is deliberate: the goal is to turn mathematical and algorithmic ideas into working solutions. Laaksonen later developed closely related material into the Springer textbook Guide to Competitive Programming, which similarly presents competitive programming as a way of learning algorithms and computational thinking. 

Key takeaways

• Algorithmic efficiency is central: understanding complexities such as $O(n)$, $O(n\log n)$ and $O(n^2)$ helps determine whether a solution is practical.
  
• A relatively small toolkit solves many problems: dynamic programming, greedy algorithms, binary search, graph traversal and appropriate data structures recur throughout competitive programming.
  
• Mathematics and programming reinforce each other: number theory, combinatorics, probability and geometry become practical tools for constructing algorithms.
  
• The book is best used actively: reading a technique and then implementing it on actual programming problems is much more valuable than reading the handbook passively.
  

Overall: ★★★★★ — An excellent free introduction and reference for anyone who wants to learn algorithms through problem solving, particularly students interested in competitive programming, informatics olympiads, or strengthening their general algorithmic skills.


BOOK [PDF]]]> https://mklab.gr/showthread.php?tid=1224 Mon, 20 Jul 2026 23:14:42 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=1224 The Clean Code Handbook 
by [freecodecamp.org]

Summary

The Clean Code Handbook by freeCodeCamp explains the principles and practices behind writing software that is readable, maintainable, and easy for other developers to understand. It emphasizes that good code is not only code that works, but code that communicates its purpose clearly through meaningful names, simple structures, and consistent organization. The guide introduces important practices such as keeping functions small, avoiding unnecessary complexity, reducing duplication, writing clear comments, and following consistent formatting rules. 

It also highlights the importance of testing, refactoring, and continuously improving code quality instead of allowing technical debt to grow. Clean code helps teams collaborate more effectively because developers can quickly understand, modify, and extend existing programs. Ultimately, the guide presents clean coding as a professional habit that improves reliability, productivity, and long-term software development success. 

HANDBOOK]]> The Clean Code Handbook 
by [freecodecamp.org]

Summary

The Clean Code Handbook by freeCodeCamp explains the principles and practices behind writing software that is readable, maintainable, and easy for other developers to understand. It emphasizes that good code is not only code that works, but code that communicates its purpose clearly through meaningful names, simple structures, and consistent organization. The guide introduces important practices such as keeping functions small, avoiding unnecessary complexity, reducing duplication, writing clear comments, and following consistent formatting rules. 

It also highlights the importance of testing, refactoring, and continuously improving code quality instead of allowing technical debt to grow. Clean code helps teams collaborate more effectively because developers can quickly understand, modify, and extend existing programs. Ultimately, the guide presents clean coding as a professional habit that improves reliability, productivity, and long-term software development success. 

HANDBOOK]]> https://mklab.gr/showthread.php?tid=902 Sun, 05 Jul 2026 20:38:34 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=902 Introduction to High Performance Scientific Computing 
by Eijkhout, Victor

Summary

Victor Eijkhout’s Introduction to High Performance Scientific Computing is essentially a roadmap for understanding how modern scientific computing actually achieves speed at scale, and why it requires much more than just writing correct code. 

The book explains that high-performance computing (HPC) sits at the intersection of mathematics, computer science, and real scientific applications: problems from physics or engineering are first translated into mathematical structures like linear algebra or differential equations, and then into algorithms that must be carefully designed to run efficiently on complex computer architectures. 

A major theme is that performance is not automatic—memory hierarchies, communication costs, and parallel execution all heavily influence whether a program scales or becomes bottlenecked. The book gradually builds intuition about parallel computing models (like shared vs distributed memory), numerical methods, and algorithmic thinking, showing how techniques such as decomposition of tasks, communication between processors, and optimization of data movement are just as important as the math itself. 

Overall, it teaches readers to think like computational scientists: not only solving equations, but also engineering solutions that can actually run on supercomputers within realistic time and resource limits. 


BOOK]]> Introduction to High Performance Scientific Computing 
by Eijkhout, Victor

Summary

Victor Eijkhout’s Introduction to High Performance Scientific Computing is essentially a roadmap for understanding how modern scientific computing actually achieves speed at scale, and why it requires much more than just writing correct code. 

The book explains that high-performance computing (HPC) sits at the intersection of mathematics, computer science, and real scientific applications: problems from physics or engineering are first translated into mathematical structures like linear algebra or differential equations, and then into algorithms that must be carefully designed to run efficiently on complex computer architectures. 

A major theme is that performance is not automatic—memory hierarchies, communication costs, and parallel execution all heavily influence whether a program scales or becomes bottlenecked. The book gradually builds intuition about parallel computing models (like shared vs distributed memory), numerical methods, and algorithmic thinking, showing how techniques such as decomposition of tasks, communication between processors, and optimization of data movement are just as important as the math itself. 

Overall, it teaches readers to think like computational scientists: not only solving equations, but also engineering solutions that can actually run on supercomputers within realistic time and resource limits. 


BOOK]]> https://mklab.gr/showthread.php?tid=714 Wed, 24 Jun 2026 15:09:36 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=714 Mathematics and Computation 
by [Avi Wigderson]

Summary

Mathematics and computation have become deeply connected forces shaping the modern world, and Avi Wigderson’s work explores this fascinating relationship through the lens of theoretical computer science. His book Mathematics and Computation: A Theory Revolutionizing Technology and Science presents how ideas from computational complexity, algorithms, and mathematical theory help us understand the possibilities and limitations of computation. 

The book reveals that computation is not only about building faster machines but also about discovering fundamental principles behind problem-solving, learning, randomness, cryptography, and artificial intelligence. By connecting mathematics with computer science, it shows how abstract theories can influence real-world technologies, from secure communication systems to modern machine learning. 

Through a clear exploration of algorithms and complexity theory, Wigderson highlights some of the biggest questions in science: what computers can achieve, what challenges remain unsolved, and how computational thinking can transform other fields. This work offers valuable insight into the future of mathematics, technology, and artificial intelligence, demonstrating why understanding computation is essential in today’s digital era. 

BOOK]]> Mathematics and Computation 
by [Avi Wigderson]

Summary

Mathematics and computation have become deeply connected forces shaping the modern world, and Avi Wigderson’s work explores this fascinating relationship through the lens of theoretical computer science. His book Mathematics and Computation: A Theory Revolutionizing Technology and Science presents how ideas from computational complexity, algorithms, and mathematical theory help us understand the possibilities and limitations of computation. 

The book reveals that computation is not only about building faster machines but also about discovering fundamental principles behind problem-solving, learning, randomness, cryptography, and artificial intelligence. By connecting mathematics with computer science, it shows how abstract theories can influence real-world technologies, from secure communication systems to modern machine learning. 

Through a clear exploration of algorithms and complexity theory, Wigderson highlights some of the biggest questions in science: what computers can achieve, what challenges remain unsolved, and how computational thinking can transform other fields. This work offers valuable insight into the future of mathematics, technology, and artificial intelligence, demonstrating why understanding computation is essential in today’s digital era. 

BOOK]]> https://mklab.gr/showthread.php?tid=690 Tue, 23 Jun 2026 18:44:33 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=690 Algorithms 
by Jeff Erickson

Summary

Modern computing runs on algorithms, yet many learners struggle to move beyond memorizing formulas and code snippets. Jeff Erickson’s Algorithms offers a deeper and more intuitive approach, showing how algorithmic thinking helps solve complex problems efficiently. Rather than focusing solely on implementation, the book explores the reasoning behind algorithm design, guiding readers through concepts such as recursion, dynamic programming, graph algorithms, shortest paths, network flows, and computational complexity. It is designed for students and aspiring computer scientists who already have a foundation in discrete mathematics and basic data structures.
What makes this algorithms textbook stand out is its emphasis on problem-solving and intuition. Readers are encouraged to understand why an algorithm works, not just how to write it. Through carefully chosen examples and challenging exercises, the book develops the analytical skills needed to tackle real-world computational challenges. Topics such as greedy algorithms, depth-first search, minimum spanning trees, and NP-hardness are presented as part of a larger framework for thinking systematically about difficult problems. 
The broader impact of learning algorithms extends far beyond computer science classrooms. Algorithmic thinking powers modern software, artificial intelligence, data analysis, cybersecurity, and countless digital technologies. By building a strong understanding of algorithm design and analysis, readers gain tools that are valuable across many technical fields. Whether you are preparing for advanced studies, software engineering interviews, or simply looking to strengthen your computational thinking skills, this book provides a comprehensive and highly respected roadmap for mastering one of the most important subjects in computer science. 

BOOK]]> Algorithms 
by Jeff Erickson

Summary

Modern computing runs on algorithms, yet many learners struggle to move beyond memorizing formulas and code snippets. Jeff Erickson’s Algorithms offers a deeper and more intuitive approach, showing how algorithmic thinking helps solve complex problems efficiently. Rather than focusing solely on implementation, the book explores the reasoning behind algorithm design, guiding readers through concepts such as recursion, dynamic programming, graph algorithms, shortest paths, network flows, and computational complexity. It is designed for students and aspiring computer scientists who already have a foundation in discrete mathematics and basic data structures.
What makes this algorithms textbook stand out is its emphasis on problem-solving and intuition. Readers are encouraged to understand why an algorithm works, not just how to write it. Through carefully chosen examples and challenging exercises, the book develops the analytical skills needed to tackle real-world computational challenges. Topics such as greedy algorithms, depth-first search, minimum spanning trees, and NP-hardness are presented as part of a larger framework for thinking systematically about difficult problems. 
The broader impact of learning algorithms extends far beyond computer science classrooms. Algorithmic thinking powers modern software, artificial intelligence, data analysis, cybersecurity, and countless digital technologies. By building a strong understanding of algorithm design and analysis, readers gain tools that are valuable across many technical fields. Whether you are preparing for advanced studies, software engineering interviews, or simply looking to strengthen your computational thinking skills, this book provides a comprehensive and highly respected roadmap for mastering one of the most important subjects in computer science. 

BOOK]]> https://mklab.gr/showthread.php?tid=363 Sun, 14 Jun 2026 18:48:00 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=363 Visualisation of sorting algorithms from Simon Willison.

PAGE]]> Visualisation of sorting algorithms from Simon Willison.

PAGE]]> https://mklab.gr/showthread.php?tid=323 Sat, 13 Jun 2026 13:35:05 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=323 The Chaos Textbook: Mathematics in the age of the computer 
Glenn Elert

Summary The "Experiments" chapter of The Chaos Hypertextbook explores how chaos emerges from simple feedback loops called one-dimensional iterated maps, where a function's output is continuously reused as its next input. As the system's control parameters are adjusted, its behavior transitions from a stable steady-state to alternating periodic cycles through a splitting process known as bifurcation. This period-doubling accelerates until the system breaks down into unpredictable chaos; however, this transition is governed by a universal constant (Feigenbaum's number), proving that the path to chaos follows strict, predictable mathematical laws regardless of the equation used.

BOOK PAGE]]> The Chaos Textbook: Mathematics in the age of the computer 
Glenn Elert

Summary The "Experiments" chapter of The Chaos Hypertextbook explores how chaos emerges from simple feedback loops called one-dimensional iterated maps, where a function's output is continuously reused as its next input. As the system's control parameters are adjusted, its behavior transitions from a stable steady-state to alternating periodic cycles through a splitting process known as bifurcation. This period-doubling accelerates until the system breaks down into unpredictable chaos; however, this transition is governed by a universal constant (Feigenbaum's number), proving that the path to chaos follows strict, predictable mathematical laws regardless of the equation used.

BOOK PAGE]]> https://mklab.gr/showthread.php?tid=320 Sat, 13 Jun 2026 13:22:45 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=320 Mathematics for Computer Science 
Eric Lehman, F. Thomson Leighton, Albert R. Meyer

Summary  Mathematics for Computer Science by Eric Lehman, F. Thomson Leighton, and Albert R. Meyer is a foundational textbook that introduces discrete mathematics, mathematical reasoning, and probability specifically for computer science and engineering

BOOK PAGE]]> Mathematics for Computer Science 
Eric Lehman, F. Thomson Leighton, Albert R. Meyer

Summary  Mathematics for Computer Science by Eric Lehman, F. Thomson Leighton, and Albert R. Meyer is a foundational textbook that introduces discrete mathematics, mathematical reasoning, and probability specifically for computer science and engineering

BOOK PAGE]]> https://mklab.gr/showthread.php?tid=319 Sat, 13 Jun 2026 13:20:37 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=319 Algorithms and Complexity 
H. Wilf

Summary Algorithms and Complexity by Herbert S. Wilf (originally published in 1994, with an Internet Edition in 1994 and a second edition in 2003) is an introductory textbook designed primarily for senior-level undergraduate and graduate students in computer science and mathematics.
The book focuses on how to design computer algorithms and analyze their "complexity"—the computational cost measured primarily in running time or storage space. It draws a critical distinction between tractable ("easy") problems, which can be solved using an algorithm with a polynomial-time guarantee, and intractable ("hard") problems, which lack such a guarantee.

BOOK PAGE]]> Algorithms and Complexity 
H. Wilf

Summary Algorithms and Complexity by Herbert S. Wilf (originally published in 1994, with an Internet Edition in 1994 and a second edition in 2003) is an introductory textbook designed primarily for senior-level undergraduate and graduate students in computer science and mathematics.
The book focuses on how to design computer algorithms and analyze their "complexity"—the computational cost measured primarily in running time or storage space. It draws a critical distinction between tractable ("easy") problems, which can be solved using an algorithm with a polynomial-time guarantee, and intractable ("hard") problems, which lack such a guarantee.

BOOK PAGE]]> https://mklab.gr/showthread.php?tid=74 Mon, 01 Jun 2026 23:02:10 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=74 Αντικειμενοστραφής Προγραμματισμός: Έννοιες και Τεχνικές
Υπότιτλος: Ανάδειξή τους με C++ και Java
Συγγραφείς: Καράλη, Ιζαμπώ

Το βιβλίο αποτελεί έναν εισαγωγικό οδηγό στον αντικειμενοστραφή προγραμματισμό (Object-Oriented Programming), σχεδιασμένο κυρίως για προπτυχιακούς φοιτητές με βασικές γνώσεις διαδικαστικού προγραμματισμού.
Η δομή του βιβλίου περιλαμβάνει:

• Θεωρητική Βάση: Παρουσίαση των καθολικών εννοιών και μηχανισμών του αντικειμενοστραφούς προγραμματισμού, ανεξάρτητα από συγκεκριμένη γλώσσα.
  
• Εστίαση στη C++: Αναλυτική εξέταση της C++ με έμφαση στην αφαίρεση δεδομένων (κλάσεις, αντικείμενα, απόκρυψη υλοποίησης), στη σύνθεση κλάσεων και στην κληρονομικότητα. Γίνεται επίσης αναφορά σε στατικά μέλη, στο σύστημα I/O και στον προγραμματισμό γενικεύσεων.
  
• Επέκταση σε Java & Άλλες Γλώσσες: Παρουσίαση των αντικειμενοστραφών χαρακτηριστικών της Java, καθώς και μια σύντομη σύνοψη στοιχείων από άλλες σχετικές γλώσσες.
  
• Πρακτική Εξάσκηση: Το βιβλίο ολοκληρώνεται με σύνθετα προβλήματα και παραδείγματα για την εμπέδωση των εννοιών.
  

SOURCE]]> Αντικειμενοστραφής Προγραμματισμός: Έννοιες και Τεχνικές
Υπότιτλος: Ανάδειξή τους με C++ και Java
Συγγραφείς: Καράλη, Ιζαμπώ

Το βιβλίο αποτελεί έναν εισαγωγικό οδηγό στον αντικειμενοστραφή προγραμματισμό (Object-Oriented Programming), σχεδιασμένο κυρίως για προπτυχιακούς φοιτητές με βασικές γνώσεις διαδικαστικού προγραμματισμού.
Η δομή του βιβλίου περιλαμβάνει:

• Θεωρητική Βάση: Παρουσίαση των καθολικών εννοιών και μηχανισμών του αντικειμενοστραφούς προγραμματισμού, ανεξάρτητα από συγκεκριμένη γλώσσα.
  
• Εστίαση στη C++: Αναλυτική εξέταση της C++ με έμφαση στην αφαίρεση δεδομένων (κλάσεις, αντικείμενα, απόκρυψη υλοποίησης), στη σύνθεση κλάσεων και στην κληρονομικότητα. Γίνεται επίσης αναφορά σε στατικά μέλη, στο σύστημα I/O και στον προγραμματισμό γενικεύσεων.
  
• Επέκταση σε Java & Άλλες Γλώσσες: Παρουσίαση των αντικειμενοστραφών χαρακτηριστικών της Java, καθώς και μια σύντομη σύνοψη στοιχείων από άλλες σχετικές γλώσσες.
  
• Πρακτική Εξάσκηση: Το βιβλίο ολοκληρώνεται με σύνθετα προβλήματα και παραδείγματα για την εμπέδωση των εννοιών.
  

SOURCE]]> https://mklab.gr/showthread.php?tid=73 Mon, 01 Jun 2026 22:53:03 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=73 Λογικός και Συναρτησιακός Προγραμματισμός
Συγγραφείς: Σταματόπουλος, Παναγιώτης

Το βιβλίο παρουσιάζει τις δύο βασικές μεθοδολογίες του δηλωτικού προγραμματισμού, οι οποίες αντιδιαστέλλονται με τον παραδοσιακό διαδικαστικό προγραμματισμό:

• Λογικός Προγραμματισμός: Εξετάζεται η φιλοσοφία του με βάση τη γλώσσα Prolog και τη λογική πρώτης τάξης. Αναλύονται θέματα υλοποίησης, παράλληλης επεξεργασίας, περιορισμών και η σημασιολογία των προγραμμάτων.
  
• Συναρτησιακός Προγραμματισμός: Παρουσιάζεται μέσω της γλώσσας Haskell. Καλύπτεται το θεωρητικό του υπόβαθρο (λάμδα λογισμός, συνδυαστές) καθώς και τεχνικές υλοποίησης (σειρές αναγωγής, αναγωγή γράφων).
  

Και οι δύο προσεγγίσεις συνδυάζουν την πρακτική εφαρμογή με την αυστηρή θεωρητική τεκμηρίωση.

SOURCE]]> Λογικός και Συναρτησιακός Προγραμματισμός
Συγγραφείς: Σταματόπουλος, Παναγιώτης

Το βιβλίο παρουσιάζει τις δύο βασικές μεθοδολογίες του δηλωτικού προγραμματισμού, οι οποίες αντιδιαστέλλονται με τον παραδοσιακό διαδικαστικό προγραμματισμό:

• Λογικός Προγραμματισμός: Εξετάζεται η φιλοσοφία του με βάση τη γλώσσα Prolog και τη λογική πρώτης τάξης. Αναλύονται θέματα υλοποίησης, παράλληλης επεξεργασίας, περιορισμών και η σημασιολογία των προγραμμάτων.
  
• Συναρτησιακός Προγραμματισμός: Παρουσιάζεται μέσω της γλώσσας Haskell. Καλύπτεται το θεωρητικό του υπόβαθρο (λάμδα λογισμός, συνδυαστές) καθώς και τεχνικές υλοποίησης (σειρές αναγωγής, αναγωγή γράφων).
  

Και οι δύο προσεγγίσεις συνδυάζουν την πρακτική εφαρμογή με την αυστηρή θεωρητική τεκμηρίωση.

SOURCE]]>