Enhance Your Learning with Program Complexity Analysis Flash Cards for quick learning
The study of analyzing the efficiency and performance characteristics of algorithms and programs.
A measure of the amount of time an algorithm takes to run as a function of the input size.
A measure of the amount of memory an algorithm requires as a function of the input size.
A mathematical notation used to describe the upper bound or worst-case scenario of an algorithm's time or space complexity.
The process of evaluating and comparing algorithms based on their efficiency and performance characteristics.
Organized and structured formats for storing, managing, and manipulating data efficiently.
Algorithms used to arrange elements in a specific order, such as ascending or descending.
Algorithms used to find the presence or location of a specific element within a collection of data.
A technique used to solve complex problems by breaking them down into simpler overlapping subproblems.
Algorithms that make locally optimal choices at each step with the hope of finding a global optimum solution.
A problem-solving technique that involves breaking a problem into smaller subproblems, solving them independently, and combining the solutions.
Algorithms used to solve problems related to graphs, such as finding the shortest path or detecting cycles.
A class of computational problems that are believed to be intractable, meaning there is no efficient algorithm to solve them.
Algorithms designed to execute multiple tasks simultaneously, taking advantage of parallel processing capabilities.
Methods and strategies used to improve the efficiency and performance of algorithms and programs.
Guidelines and recommendations for writing efficient and maintainable code, based on industry standards and experience.
A mathematical notation used to describe the limiting behavior of a function as the input size approaches infinity.
A programming technique where a function calls itself to solve a smaller instance of the same problem.
A technique used to map data to a fixed-size array, allowing for efficient retrieval and storage.
Hierarchical data structures consisting of nodes connected by edges, commonly used for representing hierarchical relationships.
Complete binary trees that satisfy the heap property, used for efficient priority queue operations.
The process of visiting all the vertices of a graph in a systematic manner.
A linear ordering of the vertices of a directed graph such that for every directed edge (u, v), vertex u comes before vertex v in the ordering.
Algorithms used to find the shortest path between two vertices in a graph.
A tree that spans all the vertices of a connected, undirected graph with the minimum possible total edge weight.
The process of allocating and deallocating memory during program execution, allowing for efficient memory usage.
The process of storing frequently accessed data in a faster memory to reduce access time and improve performance.
The use of multiple processors or computing resources to perform computations simultaneously, speeding up the execution of programs.
A technique used to solve optimization problems by systematically exploring the search space and pruning unpromising branches.
Algorithms that provide near-optimal solutions for optimization problems within a guaranteed bound.
Algorithms that use randomization to improve efficiency or provide probabilistic guarantees.
Algorithms that sort data using parallel processing techniques, improving the speed of sorting large datasets.
Algorithms that are designed to perform well on a hierarchy of memory levels, without explicit knowledge of the cache parameters.
A method for analyzing the average time complexity of a sequence of operations, even if individual operations may be expensive.
General approaches or strategies for solving problems, such as divide and conquer, dynamic programming, and greedy algorithms.
Methods and strategies for designing efficient algorithms, such as problem reduction, problem transformation, and problem decomposition.
The process of formulating problems, designing algorithms to solve them, and implementing the algorithms in a programming language.
A way of solving problems by breaking them down into smaller, more manageable subproblems and designing algorithms to solve them.
The measure of how well an algorithm solves a problem, taking into account factors such as time complexity and space complexity.
The property of an algorithm producing the correct output for all possible inputs, satisfying the problem's specifications.
The ability of an algorithm to handle unexpected or erroneous inputs without crashing or producing incorrect results.
The ability of an algorithm to handle increasing amounts of data or larger problem sizes without a significant decrease in performance.
The ability of an algorithm to adapt to different input characteristics or changing problem requirements without significant modifications.
The compromises or choices made in the design of an algorithm to optimize certain aspects, such as time complexity or space complexity.
The process of improving the efficiency or performance of an algorithm by making changes to its design or implementation.
The process of evaluating and predicting the efficiency and performance characteristics of an algorithm based on its design and implementation.
A measure of the computational resources required by an algorithm, such as time, space, or other resources.
A general approach or methodology for solving problems, often involving a specific set of techniques or strategies.
A well-defined computational task or challenge that can be solved using an algorithm.
A specific algorithm or set of algorithms that solves a given problem, producing the desired output for all possible inputs.
The process of translating an algorithm into a programming language, often involving data structures and control flow.
The process of identifying and fixing errors or bugs in an algorithm's implementation, ensuring correct and reliable behavior.
The process of evaluating an algorithm's correctness, efficiency, and performance through systematic and controlled experiments.
The process of formally proving the correctness or properties of an algorithm using mathematical logic or formal methods.
The process of representing and visualizing algorithms and their execution using diagrams, graphs, or other visual aids.
The process of emulating or simulating the behavior of an algorithm or system to study its performance or behavior under different conditions.
The process of creating mathematical or computational models to represent and analyze the behavior of algorithms or systems.
The process of designing or modifying an algorithm to take advantage of parallel processing capabilities, improving its speed or scalability.
The process of evaluating and predicting the efficiency and performance characteristics of an algorithm based on its design and implementation.