Code Complexity Optimization

GIVA runs a jewellery e-commerce catalog where bundle-discount suggestions and duplicate-listing cleanup run on every catalog sync. The functions below are all functionally correct but scale badly as the catalog grows past a few thousand SKUs.

def find_matching_bundle(products, target_price):
    # products: list of dicts like {"sku": str, "name": str, "price": float}
    best_pair = None
    best_diff = float("inf")
    for i in range(len(products)):
        for j in range(len(products)):
            if i != j:
                combined = products[i]["price"] + products[j]["price"]
                diff = abs(combined - target_price)
                if diff < best_diff:
                    best_diff = diff
                    best_pair = (products[i], products[j])
    return best_pair


def remove_duplicate_skus(products):
    seen = []
    unique = []
    for p in products:
        if p["sku"] not in seen:
            unique.append(p)
            seen.append(p["sku"])
    return unique


def catalog_summary(products):
    total_value = 0
    for p in products:
        total_value += p["price"] * products.count(p)
    return total_value

Explain the time and space complexity of each function as written, and how you would bring each one down.