Design & Analysis of Algorithms for Engineering

The computational core behind optimization, search, and data operations: asymptotic analysis, data structures, graph and dynamic-programming algorithms.

Private cohorts & on-site
Format: 3-day (8:30 a.m.–4:30 p.m.)
Level: Intermediate
Location: Scheduled on demand · on-site at your facility or a regional venue
Date(s): Not yet scheduled for open enrollment. Get notified when it is, or book it privately for your team.
Includes: Certificate of Completion · printed slide binder · take-home reference text

Get notified when this course is scheduled

One email when dates are set. Or skip the wait: run it as a private cohort, on-site at your plant.

Talk to us about a private cohort
  • One email, no sequence
  • Never shared
  • Reply within one business day

Faculty

Faculty details for this seminar will be announced with the full schedule.

Fees

Early: $1,895 (payment 4+ weeks ahead)
Standard: $2,095 (check/ACH) · $2,165 (card)

Group discount: $200 off per attendee for 3+ from the same organization.

Also Available

  • Corporate on-site delivery at your facility
  • Private cohort sessions
  • Digital curriculum licensing

Seminar Overview

Software now runs the vehicle, the plant, and the analysis — and every optimization, search, and data operation your systems perform sits on algorithmic foundations. Built from MIT OpenCourseWare's algorithms and data-structures curriculum, this seminar gives engineers the computational-thinking core they were never taught: asymptotic analysis, data structures, sorting and search, graph algorithms, dynamic programming, and NP-completeness. It is the missing prerequisite that makes your ADAS, SDV, and computational courses (and your engineers' code) faster and more defensible.

Ideal Learner

  • Software and embedded engineers on vehicle and industrial platforms
  • Engineers writing or reviewing analysis, optimization, and data tools
  • ADAS, robotics, and control engineers reasoning about computation
  • Engineering managers making algorithm and architecture trade-offs
  • R&D groups developing in-house simulation and optimization code

Learning Objectives

  • Analyze algorithm and data-structure efficiency with asymptotic bounds
  • Select the right data structures and algorithms for a task
  • Apply graph and dynamic-programming methods to engineering optimization
  • Recognize NP-hard problems and choose viable search/heuristic approaches
  • Write or specify computation that scales to production data

Consulting Sessions

Seminar attendees can sign up for individual consulting sessions with the instructor. Sessions are free for registered attendees, first-come first-served — sign up when registering by calling 248-539-0473 or during the seminar.

Seminar Outline

  • Big-O, running-time growth, and the analysis mindset
  • Why loop structure and data access dominate real performance
  • Worst-case vs. average-case in practice
  • Arrays, lists, stacks, queues, heaps, trees, hash tables
  • Choosing structures for search, ordering, and priority
  • Trade-offs in memory and time
  • Comparison sorts, linear-time sorts, and when each wins
  • Binary search and its engineering pitfalls
  • Searching large and sorted data at scale
  • Representing networks, trees, and dependencies
  • Shortest-path, minimum-spanning-tree, and traversal
  • Supply chains, networks, and dependency resolution as graphs
  • The DP pattern for optimization problems
  • Knapsack, scheduling, and resource allocation
  • Engineering trade-offs and correctness
  • What NP-complete means and why it matters
  • When exact search is infeasible
  • Greedy, genetic, and approximation approaches done honestly
  • Attendees model an engineering problem with the right structure
  • Select or design an algorithm
  • Analyze and present the scaling case

More in Track G — Computational & Quantitative Engineering

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