36 docs tagged with "optimization"

Master advanced backtracking techniques including word search, palindrome partitioning, constraint propagation, and branch-and-bound optimization.

Master advanced binary search techniques including searching in rotated arrays, finding peak elements, and solving optimization problems like capacity to ship packages.

Master advanced dynamic programming techniques including interval DP, bitmask DP, digit DP, and DP optimizations with Knuth-Yao and divide-and-conquer.

Master advanced greedy techniques including greedy with sorting, priority queue applications, task scheduling, and when greedy fails.

Master the core concepts of backtracking algorithms including state space exploration, decision trees, pruning strategies, and constraint satisfaction problems.

Fail fast rather than waiting when a condition isn't satisfied, for non-blocking operations with immediate response.

Master binary search algorithms for efficient searching and optimization. Learn fundamental and advanced binary search techniques.

Master binary search with standard templates, left/right bounds, and off-by-one handling for logarithmic search problems.

Write-through, write-behind, cache-aside, and TTL strategies for reducing database load

Essential optimization patterns: caching for latency, batching for throughput, queueing for resilience.

Services making excessive network calls for operations that could be done locally or batched.

Master fundamental dynamic programming problems including 0/1 Knapsack, Longest Common Subsequence, Longest Increasing Subsequence, Coin Change, and Edit Distance.

Master the most important greedy algorithm problems: fractional knapsack, Huffman coding, job scheduling, and interval scheduling maximization

Optimize cloud spend; balance performance with cost through architecture decisions.

Track infrastructure costs, attribute costs to services, and optimize spend.

FinOps (Financial Operations) review checklist for cloud cost optimization including resource right-sizing, reserved capacity, spot instances, data transfer, storage tiering, and budget management.

Master double-ended queues and priority queues including heap implementation, top-K problems, and running median for advanced queue applications.

Minimize synchronization overhead for already-initialized state by checking conditions before acquiring locks.

Understand overlapping subproblems, optimal substructure, memoization vs tabulation, and the core principles of dynamic programming.

Apply dynamic programming to tree and graph problems including tree DP, rerooting technique, DAG DP, and shortest paths as DP.

Master dynamic programming to solve complex optimization problems efficiently. Learn to identify DP patterns and implement optimal solutions.

Master Core Web Vitals, bundle optimization, lazy loading, and image optimization strategies to deliver fast, responsive user experiences at scale.

Master greedy algorithms for optimization problems. Learn greedy choice property, proof techniques, and classic greedy problems.

Master greedy algorithm principles including greedy choice property, optimal substructure, proof techniques, and when greedy applies.

Explore practical hash table applications including two-sum, group anagrams, and frequency counting for efficient problem-solving.

Master hash tables and sets for efficient lookups, frequency counting, and duplicate detection. Essential for optimizing many algorithmic problems.

Master practical heap applications including top-K problems, running median, merging sorted lists, and Dijkstra's algorithm.

Master advanced coding techniques including sentinel values, dummy nodes, index mapping, coordinate compression, offline processing, and meet-in-the-middle optimization.

Design indexes and shape queries to reduce IO, avoid hotspots, and meet latency SLOs.

TL;DR

Scale data systems for growth: caching, replication, sharding, and materialized views

Identify performance bottlenecks using profilers, distributed tracing, and flamegraphs

Explore practical queue applications including BFS traversal, task scheduling, and sliding window maximum with deques for efficient problem-solving.

Master sliding window technique to efficiently process contiguous subarrays and substrings, reducing time complexity from O(n²) to O(n).

Master Big O notation, complexity classes, and amortized analysis to evaluate algorithm efficiency and make informed optimization decisions.

Master the two pointers approach for efficiently solving array and string problems by reducing time complexity from O(n²) to O(n).