AP-CSP Curriculum Guides
4. Algorithms

Overview

• EU 4.1 Algorithms are precise sequences of instructions for processes that can be executed by a computer and are implemented using programming languages.
• EU 4.2 Algorithms can solve many, but not all, computational problems.

Reading from the Computer Science Field Guide

Start by reading through:

• Algorithms
• Complexity and Tractability

Learning objectives

The above chapter readings include specific knowledge for EKs marked in bold. Work to include unmarked learning objectives in the CS Field Guide is currently in progress.

LO 4.1.1 Develop an algorithm for implementation in a program

• EK 4.1.1A Sequencing, selection, and iteration are building blocks of algorithms.
• EK 4.1.1B Sequencing is the application of each step of an algorithm in the order in which the statements are given.
• EK 4.1.1C Selection uses a Boolean condition to determine which of two parts of an algorithm is used.
• EK 4.1.1D Iteration is the repetition of part of an algorithm until a condition is met or for a specified number of times.
• EK 4.1.1E Algorithms can be combined to make new algorithms.
• EK 4.1.1F Using existing correct algorithms as building blocks for constructing a new algorithm helps ensure the new algorithm is correct.
• EK 4.1.1G Knowledge of standard algorithms can help in constructing new algorithms.
• EK 4.1.1H Different algorithms can be developed to solve the same problem.
• EK 4.1.1I Developing a new algorithm to solve a problem can yield insight into the problem.

LO 4.1.2 Express an algorithm in a language.

• EK 4.1.2A Languages for algorithms include natural language, pseudocode, and visual and textual programming languages.
• EK 4.1.2B Natural language and pseudocode describe algorithms so that humans can understand them.
• K 4.1.2C Algorithms described in programming languages can be executed on a computer.
• EK 4.1.2D Different languages are better suited for expressing different algorithms.
• EK 4.1.2E Some programming languages are designed for specific domains and are better for expressing algorithms in those domains.

LO 4.2.1 Explain the difference between algorithms that run in a reasonable time and those that do not run in a reasonable time.

• EK 4.2.1A Many problems can be solved in a reasonable time.
• EK 4.2.1B Reasonable time means that the number of steps the algorithm takes is less than or equal to a polynomial function (constant, linear, square, cube, etc.) of the size of the input.

• EK 4.2.1C Some problems cannot be solved in a reasonable time, even for small input sizes.
• EK 4.2.1D Some problems can be solved but not in a reasonable time. In these cases, heuristic approaches may be helpful to find solutions in reasonable time.

LO 4.2.2 Explain the difference between solvable and unsolvable problems in computer science.

• EK 4.2.2A A heuristic is a technique that may allow us to find an approximate solution when typical methods fail to find an exact solution.
• EK 4.2.2B Heuristics may be helpful for finding an approximate solution more quickly when exact methods are too slow.

• EK 4.2.2C Some optimization problems such as "find the best" or "find the smallest" cannot be solved in a reasonable time but approximations to the optimal solution can.
• EK 4.2.2D Some problems cannot be solved using any algorithm.

LO 4.2.3 Explain the existence of undecidable problems in computer science.

• EK 4.2.3A An undecidable problem may have instances that have an algorithmic solution, but there is no algorithmic solution that solves all instances of the problem.
• EK 4.2.3B A decidable problem is one in which an algorithm can be constructed to answer "yes" or "no" for all inputs (e.g. "is the number even?").
• EK 4.2.3C An undecidable problem is one in which no algorithm can be constructed that always leads to a correct yes-or-no answer.

LO 4.2.4 Evaluate algorithms analytically and empirically for efficiency, correctness, and clarity.

• EK 4.2.4A Determining an algorithm’s efficiency is done by reasoning formally or mathematically about the algorithm.
• EK 4.2.4B Empirical analysis of an algorithm is done by implementing the algorithm and running it on different inputs.
• EK 4.2.4C The correctness of an algorithm is determined by reasoning formally or mathematically about the algorithm, not by testing an implementation of the algorithm.

• EK 4.2.4D Different correct algorithms for the same problem can have different efficiencies.
• EK 4.2.4E Sometimes, more efficient algorithms are more complex.
• EK 4.2.4F Finding an efficient algorithm for a problem can help solve larger instances of the problem.
• EK 4.2.4G Efficiency includes both execution time and memory usage.

• EK 4.2.4H Linear search can be used when searching for an item in any list; binary search can be used only when the list is sorted.