Posted on : June 13, 2025 By Tech World Times Development and Testing  Rate this post Sorting helps organize data in a specific order. It is used in search, reports, and efficient storage. Different sorting algorithms offer different performance. In this article, we will explain the Time Complexity of Sorting Algorithms in simple words. We will cover Python, Java, and C++ examples. 1. What Is Time Complexity? Time complexity tells how fast an algorithm runs. It measures the number of steps as input grows. It is written in Big-O notation. For example, O(n²) means steps grow with the square of inputs. 2. Types of Time Complexity Here are common types: O(1): Constant time O(n): Linear time O(n log n): Log-linear time O(n²): Quadratic time We will now apply these to sorting. 3. Bubble Sort Bubble Sort compares two numbers and swaps them if needed. It repeats until the list is sorted. Time Complexity: Best Case: O(n) (if already sorted) Average Case: O(n²) Worst Case: O(n²) Python Example: pythonCopyEditdef bubble_sort(arr): n = len(arr) for i in range(n): for j in range(n - i - 1): if arr[j] > arr[j+1]: arr[j], arr[j+1] = arr[j+1], arr[j] Java Example: javaCopyEditvoid bubbleSort(int arr[]) { int n = arr.length; for (int i = 0; i < n-1; i++) for (int j = 0; j < n-i-1; j++) if (arr[j] > arr[j+1]) { int temp = arr[j]; arr[j] = arr[j+1]; arr[j+1] = temp; } } C++ Example: cppCopyEditvoid bubbleSort(int arr[], int n) { for (int i = 0; i < n-1; i++) for (int j = 0; j < n-i-1; j++) if (arr[j] > arr[j+1]) swap(arr[j], arr[j+1]); } 4. Selection Sort This sort picks the smallest number and places it at the front. Time Complexity: Best Case: O(n²) Average Case: O(n²) Worst Case: O(n²) Python Example: pythonCopyEditdef selection_sort(arr): for i in range(len(arr)): min_idx = i for j in range(i+1, len(arr)): if arr[j] < arr[min_idx]: min_idx = j arr[i], arr[min_idx] = arr[min_idx], arr[i] 5. Insertion Sort This algorithm builds the final list one item at a time. Time Complexity: Best Case: O(n) Average Case: O(n²) Worst Case: O(n²) Java Example: javaCopyEditvoid insertionSort(int arr[]) { for (int i = 1; i < arr.length; i++) { int key = arr[i]; int j = i - 1; while (j >= 0 && arr[j] > key) { arr[j + 1] = arr[j]; j = j - 1; } arr[j + 1] = key; } } 6. Merge Sort Merge Sort splits the array into halves and merges them back in order. Time Complexity of Sorting Algorithms like Merge Sort is usually better. Best Case: O(n log n) Average Case: O(n log n) Worst Case: O(n log n) Python Example: pythonCopyEditdef merge_sort(arr): if len(arr) > 1: mid = len(arr) // 2 left = arr[:mid] right = arr[mid:] merge_sort(left) merge_sort(right) i = j = k = 0 while i < len(left) and j < len(right): if left[i] < right[j]: arr[k] = left[i] i += 1 else: arr[k] = right[j] j += 1 k += 1 arr[k:] = left[i:] + right[j:] 7. Quick Sort Quick Sort picks a pivot and places smaller numbers before it. Time Complexity: Best Case: O(n log n) Average Case: O(n log n) Worst Case: O(n²) C++ Example: cppCopyEditint partition(int arr[], int low, int high) { int pivot = arr[high]; int i = low - 1; for (int j = low; j < high; j++) { if (arr[j] < pivot) { i++; swap(arr[i], arr[j]); } } swap(arr[i+1], arr[high]); return i + 1; } void quickSort(int arr[], int low, int high) { if (low < high) { int pi = partition(arr, low, high); quickSort(arr, low, pi - 1); quickSort(arr, pi + 1, high); } } 8. Built-in Sort Methods Languages have built-in sort functions. These are well-optimized. Python: sorted() or list.sort() uses TimSort Time Complexity: O(n log n) Java: Arrays.sort() uses Dual-Pivot QuickSort Time Complexity: O(n log n) C++: std::sort() uses IntroSort Time Complexity: O(n log n) These are better for most real-world tasks. 9. Time Complexity Comparison Table AlgorithmBestAverageWorstStableBubble SortO(n)O(n²)O(n²)YesSelection SortO(n²)O(n²)O(n²)NoInsertion SortO(n)O(n²)O(n²)YesMerge SortO(n log n)O(n log n)O(n log n)YesQuick SortO(n log n)O(n log n)O(n²)NoTimSort (Python)O(n)O(n log n)O(n log n)YesIntroSort (C++)O(n log n)O(n log n)O(n log n)No 10. How to Choose the Right Algorithm? Use Merge Sort for large stable data. Use Quick Sort for faster average speed. Use Insertion Sort for small or nearly sorted lists. Use built-in sort functions unless you need control. Conclusion The Time Complexity of Sorting Algorithms helps us pick the right tool. Bubble, Selection, and Insertion Sort are simple but slow. Merge and Quick Sort are faster and used often. Built-in functions are highly optimized. Python, Java, and C++ each have their strengths. Understand your problem and input size. Then pick the sorting method. This ensures better speed and performance in your code. Tech World TimesTech World Times (TWT), a global collective focusing on the latest tech news and trends in blockchain, Fintech, Development & Testing, AI and Startups. If you are looking for the guest post then contact at techworldtimes@gmail.com

Development and Testing  Rate this post Sorting is a basic task in programming. It arranges data in order. There are many sorting algorithms. Selection Sort is one of the simplest sorting methods. It is easy to understand and code. But it is not the fastest. In this guide, we will explain the Selection Sort Time Complexity. We will cover best, worst, and average cases. What Is Selection Sort? Selection Sort works by selecting the smallest element from the list. It places it in the correct position. It repeats this process for all elements. One by one, it moves the smallest values to the front. Let’s see an example: Input: [5, 3, 8, 2]Step 1: Smallest is 2 → swap with 5 → [2, 3, 8, 5]Step 2: Smallest in remaining is 3 → already correctStep 3: Smallest in remaining is 5 → swap with 8 → [2, 3, 5, 8] Now the list is sorted.How Selection Sort Works Selection Sort uses two loops. The outer loop moves one index at a time. The inner loop finds the smallest element. After each pass, the smallest value is moved to the front. The position is fixed. Selection Sort does not care if the list is sorted or not. It always does the same steps. Selection Sort Algorithm Here is the basic algorithm: Start from the first element Find the smallest in the rest of the list Swap it with the current element Repeat for each element This repeats until all elements are sorted. Selection Sort Code (Java Example) javaCopyEditpublic class SelectionSort { public static void sort(int[] arr) { int n = arr.length; for (int i = 0; i < n - 1; i++) { int min = i; for (int j = i + 1; j < n; j++) { if (arr[j] < arr[min]) { min = j; } } int temp = arr[min]; arr[min] = arr[i]; arr[i] = temp; } } } This code uses two loops. The outer loop runs n-1 times. The inner loop finds the minimum. Selection Sort Time Complexity Now let’s understand the main topic. Let’s analyze Selection Sort Time Complexity in three cases. 1. Best Case Even if the array is already sorted, Selection Sort checks all elements. It keeps comparing and swapping. Time Complexity: O(n²) Reason: Inner loop runs fully, regardless of the order Example Input: [1, 2, 3, 4, 5] Even here, every comparison still happens. Only fewer swaps occur, but comparisons remain the same. 2. Worst Case This happens when the array is in reverse order. But Selection Sort does not optimize for this. Time Complexity: O(n²) Reason: Still needs full comparisons Example Input: [5, 4, 3, 2, 1] Even in reverse, the steps are the same. It compares and finds the smallest element every time. 3. Average Case This is when elements are randomly placed. It is the most common scenario in real-world problems. Time Complexity: O(n²) Reason: Still compares each element in the inner loop Example Input: [3, 1, 4, 2, 5] Selection Sort does not change behavior based on input order. So the complexity remains the same. Why Is It Always O(n²)? Selection Sort compares all pairs of elements. The number of comparisons does not change. Total comparisons = n × (n – 1) / 2 That’s why the time complexity is always O(n²).It does not reduce steps in any case. It does not take advantage of sorted elements. Space Complexity Selection Sort does not need extra space. It sorts in place. Space Complexity: O(1) Only a few variables are used No extra arrays or memory needed This is one good point of the Selection Sort. Comparison with Other Algorithms Let’s compare Selection Sort with other basic sorts: AlgorithmBest CaseAverage CaseWorst CaseSpaceSelection SortO(n²)O(n²)O(n²)O(1)Bubble SortO(n)O(n²)O(n²)O(1)Insertion SortO(n)O(n²)O(n²)O(1)Merge SortO(n log n)O(n log n)O(n log n)O(n)Quick SortO(n log n)O(n log n)O(n²)O(log n) As you see, Selection Sort is slower than Merge Sort and Quick Sort. Advantages of Selection Sort Very simple and easy to understand Works well with small datasets Needs very little memory Good for learning purposes Disadvantages of Selection Sort Slow on large datasets Always takes the same time, even if sorted Not efficient for real-world use When to Use Selection Sort Use Selection Sort when: You are working with a very small dataset You want to teach or learn sorting logic You want stable, low-memory sorting Avoid it for: Large datasets Performance-sensitive programs Conclusion Selection Sort Time Complexity is simple to understand. But it is not efficient for big problems. It always takes O(n²) time, no matter the case. That is the same for best, worst, and average inputs. Still, it is useful in some cases. It’s great for learning sorting basics. It uses very little memory. If you’re working with small arrays, Selection Sort is fine. For large data, use better algorithms. Understanding its time complexity helps you choose the right algorithm. Always pick the tool that fits your task. Tech World TimesTech World Times (TWT), a global collective focusing on the latest tech news and trends in blockchain, Fintech, Development & Testing, AI and Startups. If you are looking for the guest post then contact at techworldtimes@gmail.com

There are a few things an interior designer wouldn’t dare put in a bathroom. Carpet? Definitely not. Only overhead lighting? Design blasphemy. But there is one feature that finds its way into the bathroom all the time—rarely questioned, though maybe it should be—and that’s artwork. We get it: who doesn’t want to add a little personality to a space that otherwise is quite functional? Still, design fans are often split on the addition, especially when it comes to certain types of art. Related StoriesAn oil painting resting above a clawfoot bathtub or a framed graphic print next to a mirror infuses your bathroom with warmth and storytelling, a very necessary addition to a space that's often centered around pure function. “In a bathroom, where surfaces tend to be hard and the layout driven by function, a thoughtful piece can shift the entire ambience,” shares interior designer Linette Dai. “It brings dimension to the everyday.”According to designer Ali Milch, art can transform the entire experience from “routine to restorative.” But, is it the bathroom the best (read: most hygienic) place to put a favorite photo or heirloom painting? With moisture in the mix and potential for it being in the “splash zone” (sorry, but it's true), you need to be considerate of the art you bring in and where it’s placed. To help guide your curation, we chatted with interior designers and experts on how to integrate art into your space in a way that is both beautiful and bathroom-appropriate.Be Wary of HumidityMaybe this one is obvious, but when placing art in the bathroom, be sure to look for materials that aren’t prone to water damage. “We recommend framing art with a sealed backing and UV-protective acrylic instead of glass, which is both lighter and more resistant to moisture—an important consideration in steamy bathrooms,” Cathy Glazer, founder of Artfully Walls, shares. “Plus, acrylic is much safer than glass if dropped, especially on hard tile floors, as it won’t shatter.”Dai agrees that acrylic is the way to go when putting framed works into the bathroom, “I usually recommend acrylic glazing to avoid moisture damage. For humid environments, prints or photography mounted directly on aluminum or face-mounted under acrylic are durable and beautiful.”Make It Your Creative CanvasCourtsey of Ali MilchUnless you have a sprawling space, chances are your bathroom’s square footage is limited. Rather than viewing this as a constraint, think about it as an opportunity to get creative. “Because they’re smaller and more self-contained, [bathrooms] invite experimentation—think unexpected pieces, playful themes, or striking colors,” shares Glazer. “Art helps turn the bathroom into a moment of surprise and style.”“It doesn’t have to feel stuffy or overly formal,” Milch adds. “In a recent Tribeca project, we installed a kitschy iMessage bubble with the text ‘I love you too’ on the wall facing the entry. It’s a lighthearted, personal touch.”While it’s fun to get whimsical with your bathroom art (pro tip: secondhand stores can be a great place for unique finds), Dai also suggests still approaching it with a curated eye and saving anything that is precious or too high-maintenance for the powder room. “In full baths, I tend to be more selective based on how the space is ventilated and used day-to-day,” she shares. “Powder rooms, on the other hand, offer more freedom. That’s where I love incorporating oil paintings. They bring soul and a sense of history, and can make even the smallest space feel elevated.”Keep Materials And Size In MindAnother material worth considering adding? Ceramics. “Ceramic pieces also work beautifully, especially when there’s open shelving or decorative niches to display them,” shares Milch. Be wary of larger-scale sculptures, as they could potentially be slightly disruptive to the space. “Any type of artwork can work in a bathroom depending on the spatial allowances, but the typical bathroom is suited to wall hangings versus sculptures,” says Sarah Latham of L Interiors.And don’t forget to be mindful of scale. “As for size, I always opt for larger pieces in smaller spaces, it may feel counter-intuitive, but it makes a tight space feel larger,” Anastasia Casey of The Interior Collective shares. “I look for works that complement the finishes and palette without overwhelming it.”Let It Set The ToneCourtesy of Annie SloanArtwork in the bathroom doesn’t just decorate it; it can define it. “In bathrooms, there’s often less visual competition—no bold furniture or patterned textiles—so the art naturally becomes more of a focal point,” Dai adds. “That’s why the mood it sets matters so much. I think more intentionally about subject matter—what someone will see up close, often in moments of solitude,” shares Dai. Whether it’s a serene landscape photo or storied painting, don’t underestimate what a piece of art can do for the most utilitarian room in the house. With the right materials and placement, it can hold its own—moisture and all—while adding a design moment and feels considered and unexpected.Follow House Beautiful on Instagram and TikTok.

As AI faces court challenges from Disney and Universal, legal battles are shaping the industry's future | Opinion Silicon advances and design innovations do still push us forward – but the future landscape of the industry is also being sculpted in courtrooms and parliaments Image credit: Disney / Epic Games Opinion by Rob Fahey Contributing Editor Published on June 13, 2025 In some regards, the past couple of weeks have felt rather reassuring. We've just seen a hugely successful launch for a new Nintendo console, replete with long queues for midnight sales events. Over the next few days, the various summer events and showcases that have sprouted amongst the scattered bones of E3 generated waves of interest and hype for a host of new games. It all feels like old times. It's enough to make you imagine that while change is the only constant, at least it's we're facing change that's fairly well understood, change in the form of faster, cheaper silicon, or bigger, more ambitious games. If only the winds that blow through this industry all came from such well-defined points on the compass. Nestled in amongst the week's headlines, though, was something that's likely to have profound but much harder to understand impacts on this industry and many others over the coming years – a lawsuit being brought by Disney and NBC Universal against Midjourney, operators of the eponymous generative AI image creation tool. In some regards, the lawsuit looks fairly straightforward; the arguments made and considered in reaching its outcome, though, may have a profound impact on both the ability of creatives and media companies (including game studios and publishers) to protect their IP rights from a very new kind of threat, and the ways in which a promising but highly controversial and risky new set of development and creative tools can be used commercially. A more likely tack on Midjourney's side will be the argument that they are not responsible for what their customers create with the tool I say the lawsuit looks straightforward from some angles, but honestly overall it looks fairly open and shut – the media giants accuse Midjourney of replicating their copyrighted characters and material, and of essentially building a machine for churning out limitless copyright violations. The evidence submitted includes screenshot after screenshot of Midjourney generating pages of images of famous copyrighted and trademarked characters ranging from Yoda to Homer Simpson, so "no we didn't" isn't going to be much of a defence strategy here. A more likely tack on Midjourney's side will be the argument that they are not responsible for what their customers create with the tool – you don't sue the manufacturers of oil paints or canvases when artists use them to paint something copyright-infringing, nor does Microsoft get sued when someone writes something libellous in Word, and Midjourney may try to argue that their software belongs in that tool category, with users alone being ultimately responsible for how they use them. If that argument prevails and survives appeals and challenges, it would be a major triumph for the nascent generative AI industry and a hugely damaging blow to IP holders and creatives, since it would seriously undermine their argument that AI companies shouldn't be able to include copyrighted material into training data sets without licensing or compensation. The reason Disney and NBCU are going after Midjourney specifically seems to be partially down to Midjourney being especially reticent to negotiate with them about licensing fees and prompt restrictions; other generative AI firms have started talking, at least, about paying for content licenses for training data, and have imposed various limitations on their software to prevent the most egregious and obvious forms of copyright violation (at least for famous characters belonging to rich companies; if you're an individual or a smaller company, it's entirely the Wild West out there as regards your IP rights). In the process, though, they're essentially risking a court showdown over a set of not-quite-clear legal questions at the heart of this dispute, and if Midjourney were to prevail in that argument, other AI companies would likely back off from engaging with IP holders on this topic. To be clear, though, it seems highly unlikely that Midjourney will win that argument, at least not in the medium to long term. Yet depending on how this case moves forward, losing the argument could have equally dramatic consequences – especially if the courts find themselves compelled to consider the question of how, exactly, a generative AI system reproduces a copyrighted character with such precision without storing copyright-infringing data in some manner. The 2020s are turning out to be the decade in which many key regulatory issues come to a head all at once AI advocates have been trying to handwave around this notion from the outset, but at some point a court is going to have to sit down and confront the fact that the precision with which these systems can replicate copyrighted characters, scenes, and other materials requires that they must have stored that infringing material in some form. That it's stored as a scattered mesh of probabilities across the vertices of a high-dimensional vector array, rather than a straightforward, monolithic media file, is clearly important but may ultimately be considered moot. If the data is in the system and can be replicated on request, how that differs from Napster or The Pirate Bay is arguably just a matter of technical obfuscation. Not having to defend that technical argument in court thus far has been a huge boon to the generative AI field; if it is knocked over in that venue, it will have knock-on effects on every company in the sector and on every business that uses their products. Nobody can be quite sure which of the various rocks and pebbles being kicked on this slope is going to set off the landslide, but there seems to be an increasing consensus that a legal and regulatory reckoning is coming for generative AI. Consequently, a lot of what's happening in that market right now has the feel of companies desperately trying to establish products and lock in revenue streams before that happens, because it'll be harder to regulate a technology that's genuinely integrated into the world's economic systems than it is to impose limits on one that's currently only clocking up relatively paltry sales and revenues. Keeping an eye on this is crucial for any industry that's started experimenting with AI in its workflows – none more than a creative industry like video games, where various forms of AI usage have been posited, although the enthusiasm and buzz so far massively outweighs any tangible benefits from the technology. Regardless of what happens in legal and regulatory contexts, AI is already a double-edged sword for any creative industry. Used judiciously, it might help to speed up development processes and reduce overheads. Applied in a slapdash or thoughtless manner, it can and will end up wreaking havoc on development timelines, filling up storefronts with endless waves of vaguely-copyright-infringing slop, and potentially make creative firms, from the industry's biggest companies to its smallest indie developers, into victims of impossibly large-scale copyright infringement rather than beneficiaries of a new wave of technology-fuelled productivity. The legal threat now hanging over the sector isn't new, merely amplified. We've known for a long time that AI generated artwork, code, and text has significant problems from the perspective of intellectual property rights (you can infringe someone else's copyright with it, but generally can't impose your own copyright on its creations – opening careless companies up to a risk of having key assets in their game being technically public domain and impossible to protect). Even if you're not using AI yourself, however – even if you're vehemently opposed to it on moral and ethical grounds (which is entirely valid given the highly dubious land-grab these companies have done for their training data), the Midjourney judgement and its fallout may well impact the creative work you produce yourself and how it ends up being used and abused by these products in future. This all has huge ramifications for the games business and will shape everything from how games are created to how IP can be protected for many years to come – a wind of change that's very different and vastly more unpredictable than those we're accustomed to. It's a reminder of just how much of the industry's future is currently being shaped not in development studios and semiconductor labs, but rather in courtrooms and parliamentary committees. The ways in which generative AI can be used and how copyright can persist in the face of it will be fundamentally shaped in courts and parliaments, but it's far from the only crucially important topic being hashed out in those venues. The ongoing legal turmoil over the opening up of mobile app ecosystems, too, will have huge impacts on the games industry. Meanwhile, the debates over loot boxes, gambling, and various consumer protection aspects related to free-to-play models continue to rumble on in the background. Because the industry moves fast while governments move slow, it's easy to forget that that's still an active topic for as far as governments are concerned, and hammers may come down at any time. Regulation by governments, whether through the passage of new legislation or the interpretation of existing laws in the courts, has always loomed in the background of any major industry, especially one with strong cultural relevance. The games industry is no stranger to that being part of the background heartbeat of the business. The 2020s, however, are turning out to be the decade in which many key regulatory issues come to a head all at once, whether it's AI and copyright, app stores and walled gardens, or loot boxes and IAP-based business models. Rulings on those topics in various different global markets will create a complex new landscape that will shape the winds that blow through the business, and how things look in the 2030s and beyond will be fundamentally impacted by those decisions.

The smallest electric cargo bike is back, and it’s safer than ever.