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Let’s dive into the nuances that make the iced latte such a beloved drink. It is a delightful concoction made with espresso and chilled milk, served over ice. The iced latte, known for its velvety smoothness and comforting flavor, is a popular choice among coffee enthusiasts. Picture yourself sitting in a cozy café, savoring the aromas of freshly brewed coffee, as I guide you through the distinguishing characteristics of these two delectable drinks. Let’s embark on a journey to explore the key differences between the iced latte and the iced macchiato. Varies based on milk and added sweetenersĪ smooth and creamy iced coffee drink with a less pronounced coffee flavor.Ī strong coffee flavor with a creamy and velvety texture.ĭifference between Iced Latte and Iced Macchiato Layered appearance with contrasting colors Generally less sweet, focuses on the espresso Here’s a quick table summarizing the key differences between an Iced Latte and an Iced Macchiato:īold and rich, with a layered presentation So, if you’re ready to learn about the difference between an iced latte and an iced macchiato, read on! By the end of this post, you’ll be able to confidently order your favorite iced coffee drink and impress your friends with your coffee knowledge. Whether you’re a seasoned coffee connoisseur or just starting to explore the world of coffee, this post is for you. You may have heard these terms before, but do you really know the difference between the two? 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When debugging C or C++ code in CLion, you can explore the underlying assembly code. Very cool and slick for developing hardware-related nodes directly on the RPi but from your desktop CLion. docker build -t clion/remote-cpp-env:0.5 -f Dockerfile.remote-cpp-env. CLion can use ssh and it will rsync code to the target and use gdb over ssh. It can also run in both windowed and full-screen mode. Also, I got full remote build/debugging of nodes on remote targets such as RPi using CLion. More than allowing you to launch it on modern computers, the app comes built with enhanced stability thanks to the emulation of the old DOS environment. It also incorporates a few project samples. Additionally, it comes with code inspection, watches, and tracing features. As mentioned, it carries all the features and toolsets of the original Borland app, including a compiler, a debugger, the DOS shell, and breakpoints. Turbo C++ is one of the most reliable alternatives to manually attempting to solve Turbo C++'s incompatibility issues with modern Windows computers. Luckily, there are a few workarounds that you can use. Unfortunately, the program's compatibility with the latest Windows versions is debatable. Software development tools may have surpassed them over the years, but still the Borland app has its uses today. The developer Borland achieved immense success with the release of Turbo and Borland C++, which were, at the time, the most reliable IDE platforms for amateur and professional programmers. But since ode45 is always interpolating in order to make the plots look nicer, this would get in the way. So that begs the question, why wasn’t this used in the MATLAB ODE Suite (it’s clear Shampine new about it!)? (I actually asked him in an email) The reason is because its interpolation requires calculating some extra steps, so it’s less efficient if you are ALWAYS interpolating. However, in 1999, the same year the MATLAB ODE Suite was published, Shampine released a paper with a new 5(4) pair which was more efficient than the Dormand-Prince method. ode45 is based on the Dormand-Prince 5(4) pair. There’s a very good example of this in ode45. For one thing, these modifications like adding extra points to the solution array can really increase the amount of memory consumed if the ODE system is large! This actually has an impact in other ways. While MATLAB does an excellent job at giving a large amount of easily available functionality, where it lacks is performance. There are also dde23 and ddesd for delay differential equations, and in the financial toolbox there’s an Euler-Maruyama method for SDEs. Every one of these methods is setup with event handling, and there are methods which can handle differential-algebraic equations. You’re given access to the “dense output function” (this is the function which computes the interpolations). The methods allow you to use complex numbers. Shampine’s scheme is a good quick fix to this problem which most people probably never knew was occurring under the hood! This is because high order ODE solvers are good enough at achieving “standard user error tolerances” that they actually achieve quite large timesteps, and in doing so step too infrequently to make a good plot. So between any two steps that the solver takes, it automatically adds in 4 extra points using a 4th order interpolation. When you solve an equation using ode45, the Runge-Kutta method uses a “free” interpolation to fill in some extra points. That method just works and creates good plots, right? Well, Shampine added a little trick to it. Let’s take for example the classic ode45. MATLAB documents its ODE solvers very well, there’s a similar interface for using each of the different methods, and it tells you in a table in which cases you should use the different methods.īut the modifications to the methods goes even further. The MATLAB ODE Suite does extremely well at hitting these goals. Instead of focusing on efficiency, they key for this group is to have a clear and neatly defined (universal) interface which has a lot of flexibility. The idea is pretty simple: users of a problem solving environment (the examples from his papers are MATLAB and Maple) do not have the same requirements as more general users of scientific computing. Shampine also had a few other papers at this time developing the idea of a “methods for a problem solving environment” or a PSE. MATLAB’s differential equation solver suite was described in a research paper by its creator Lawerance Shampine, and this paper is one of the most highly cited SIAM Scientific Computing publications. You can find it here (click for PDF):ĭue to its popularity, let’s start with MATLAB’s built in differential equation solvers. If you just want a quick summary, I created a table which has all of this information. You will see at the end that DifferentialEquations.jl does offer pretty much everything from the other suite combined, but that’s no accident: our software organization came last and we used these suites as a guiding hand for how to design ours.) Quick Summary Table (Full disclosure, I am the lead developer of DifferentialEquations.jl. I hope that by giving you the details for how each suite was put together (and the “why”, as gathered from software publications) you can come to your own conclusion as to which suites are right for you. This is a good way to reflect upon what’s available and find out where there is room for improvement. What I would like to do is take the time to compare and contrast between the most popular offerings. For the field of scientific computing, the methods for solving differential equations are what’s important. Many times a scientist is choosing a programming language or a software for a specific purpose. |