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import numpy as np
from scipy.optimize import minimize

class RevenueGrowthModel:
    """
    Multi-variable revenue growth optimization model
    """

    def __init__(self, base_revenue: float, market_size: float, competitive_position: float):
        self.base_revenue = base_revenue
        self.market_size = market_size
        self.competitive_position = competitive_position

    def growth_function(self, variables: list) -> float:
        """
        Revenue growth function with multiple variables:
        - customer_acquisition_rate: % monthly customer growth
        - price_optimization: % price increase capability
        - market_expansion: % market expansion opportunity
        - retention_improvement: % customer retention enhancement
        """
        ca_rate, price_opt, market_exp, retention_imp = variables

        # Customer base growth with retention
        customer_growth = ca_rate * (1 + retention_imp)

        # Revenue per customer with pricing optimization
        revenue_per_customer = self.base_revenue * (1 + price_opt)

        # Market expansion multiplier
        market_multiplier = 1 + (market_exp * self.competitive_position)

        # Total revenue growth calculation
        total_growth = customer_growth * revenue_per_customer * market_multiplier

        return total_growth

    def optimize_growth_strategy(self, target_growth: float, constraints: dict) -> dict:
        """
        Optimize growth strategy to achieve target growth within constraints
        """
        def objective(variables):
            predicted_growth = self.growth_function(variables)
            return abs(predicted_growth - target_growth)

        # Variable constraints: [ca_rate, price_opt, market_exp, retention_imp]
        bounds = [
            (constraints.get('min_ca_rate', 0.01), constraints.get('max_ca_rate', 0.20)),
            (constraints.get('min_price_opt', -0.10), constraints.get('max_price_opt', 0.15)),
            (constraints.get('min_market_exp', 0.0), constraints.get('max_market_exp', 0.50)),
            (constraints.get('min_retention', 0.0), constraints.get('max_retention', 0.30))
        ]

        initial_guess = [0.10, 0.05, 0.20, 0.15]

        result = minimize(objective, initial_guess, bounds=bounds, method='L-BFGS-B')

        return {
            'optimal_ca_rate': result.x[0],
            'optimal_price_optimization': result.x[1],
            'optimal_market_expansion': result.x[2],
            'optimal_retention_improvement': result.x[3],
            'predicted_growth': self.growth_function(result.x),
            'optimization_success': result.success
        }

# Example implementation for technology startup
startup_revenue_model = RevenueGrowthModel(
    base_revenue=5000000,  # $5M current ARR
    market_size=1000000000,  # $1B total addressable market
    competitive_position=0.15  # 15% competitive advantage strength
)

growth_constraints = {
    'min_ca_rate': 0.05,     # Minimum 5% monthly customer acquisition
    'max_ca_rate': 0.25,     # Maximum 25% monthly customer acquisition
    'min_price_opt': -0.05,  # Maximum 5% price decrease
    'max_price_opt': 0.20,   # Maximum 20% price increase
    'min_market_exp': 0.10,  # Minimum 10% market expansion
    'max_market_exp': 0.40,  # Maximum 40% market expansion
    'min_retention': 0.10,   # Minimum 10% retention improvement
    'max_retention': 0.35    # Maximum 35% retention improvement
}

optimal_strategy = startup_revenue_model.optimize_growth_strategy(
    target_growth=0.40,  # 40% annual growth target
    constraints=growth_constraints
)

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class MarketPenetrationModel:
    """
    Market penetration and competitive positioning optimization
    """

    def __init__(self, current_market_share: float, total_market_size: float,
                 competitive_landscape: dict):
        self.current_market_share = current_market_share
        self.total_market_size = total_market_size
        self.competitive_landscape = competitive_landscape

    def penetration_model(self, investment_allocation: dict) -> dict:
        """
        Model market penetration based on investment allocation:
        - marketing_investment: Marketing and customer acquisition investment
        - product_investment: Product development and differentiation investment
        - sales_investment: Sales team and channel development investment
        - technology_investment: Technology and platform investment
        """

        # Market response curves (diminishing returns)
        marketing_impact = np.log(1 + investment_allocation['marketing']) * 0.15
        product_impact = np.log(1 + investment_allocation['product']) * 0.20
        sales_impact = np.log(1 + investment_allocation['sales']) * 0.12
        technology_impact = np.log(1 + investment_allocation['technology']) * 0.18

        # Competitive response factor
        competitive_pressure = sum(self.competitive_landscape.values()) / len(self.competitive_landscape)
        competitive_factor = 1 - (competitive_pressure * 0.3)

        # Market penetration calculation
        total_impact = (marketing_impact + product_impact + sales_impact + technology_impact)
        market_share_increase = total_impact * competitive_factor

        new_market_share = min(self.current_market_share + market_share_increase, 0.40)

        return {
            'new_market_share': new_market_share,
            'market_share_increase': market_share_increase,
            'revenue_impact': new_market_share * self.total_market_size,
            'competitive_factor': competitive_factor,
            'investment_efficiency': market_share_increase / sum(investment_allocation.values())
        }

    def optimize_investment_allocation(self, total_budget: float,
                                     target_market_share: float) -> dict:
        """
        Optimize investment allocation to achieve target market share
        """
        def objective(allocation):
            investment_dict = {
                'marketing': allocation[0] * total_budget,
                'product': allocation[1] * total_budget,
                'sales': allocation[2] * total_budget,
                'technology': allocation[3] * total_budget
            }

            result = self.penetration_model(investment_dict)
            return abs(result['new_market_share'] - target_market_share)

        # Allocation constraints (percentages must sum to 1.0)
        constraints = ({'type': 'eq', 'fun': lambda x: sum(x) - 1.0})
        bounds = [(0.1, 0.6), (0.1, 0.5), (0.1, 0.4), (0.1, 0.4)]

        initial_guess = [0.25, 0.25, 0.25, 0.25]

        result = minimize(objective, initial_guess, bounds=bounds,
                         constraints=constraints, method='SLSQP')

        optimal_allocation = {
            'marketing': result.x[0] * total_budget,
            'product': result.x[1] * total_budget,
            'sales': result.x[2] * total_budget,
            'technology': result.x[3] * total_budget
        }

        final_result = self.penetration_model(optimal_allocation)

        return {
            'optimal_allocation': optimal_allocation,
            'allocation_percentages': result.x,
            'predicted_market_share': final_result['new_market_share'],
            'investment_efficiency': final_result['investment_efficiency'],
            'optimization_success': result.success
        }

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import cvxpy as cp

class CapitalAllocationOptimizer:
    """
    Capital allocation optimization using convex optimization
    """

    def __init__(self, investment_opportunities: list, risk_tolerance: float):
        self.opportunities = investment_opportunities
        self.risk_tolerance = risk_tolerance

    def optimize_portfolio(self, total_capital: float, constraints: dict) -> dict:
        """
        Optimize capital allocation across investment opportunities
        """
        n_investments = len(self.opportunities)

        # Decision variables (allocation amounts)
        allocation = cp.Variable(n_investments, nonneg=True)

        # Expected returns and risk (covariance matrix)
        expected_returns = np.array([opp['expected_return'] for opp in self.opportunities])
        risk_matrix = np.array([opp.get('risk_factors', [1.0] * n_investments)
                               for opp in self.opportunities])

        # Objective: Maximize expected return
        portfolio_return = expected_returns @ allocation
        portfolio_risk = cp.quad_form(allocation, risk_matrix)

        # Risk-adjusted objective
        objective = cp.Maximize(portfolio_return - (self.risk_tolerance * portfolio_risk))

        # Constraints
        constraints_list = [
            allocation >= 0,  # Non-negative allocations
            cp.sum(allocation) <= total_capital,  # Budget constraint
        ]

        # Minimum allocation constraints
        if 'min_allocations' in constraints:
            for i, min_alloc in enumerate(constraints['min_allocations']):
                constraints_list.append(allocation[i] >= min_alloc)

        # Maximum allocation constraints
        if 'max_allocations' in constraints:
            for i, max_alloc in enumerate(constraints['max_allocations']):
                constraints_list.append(allocation[i] <= max_alloc)

        # Diversification constraints
        if 'max_single_allocation' in constraints:
            for i in range(n_investments):
                constraints_list.append(allocation[i] <= constraints['max_single_allocation'])

        # Solve optimization problem
        problem = cp.Problem(objective, constraints_list)
        problem.solve()

        if problem.status == cp.OPTIMAL:
            optimal_allocation = allocation.value

            return {
                'optimal_allocation': optimal_allocation.tolist(),
                'expected_return': float(expected_returns @ optimal_allocation),
                'portfolio_risk': float(cp.quad_form(allocation, risk_matrix).value),
                'allocation_percentages': (optimal_allocation / total_capital).tolist(),
                'optimization_status': 'optimal',
                'total_allocated': float(np.sum(optimal_allocation))
            }
        else:
            return {
                'optimization_status': problem.status,
                'error': 'Optimization failed'
            }

# Example: Technology company capital allocation
investment_opportunities = [
    {'name': 'Core Product Development', 'expected_return': 0.25, 'risk_factors': [0.8, 0.1, 0.1, 0.0]},
    {'name': 'Market Expansion', 'expected_return': 0.35, 'risk_factors': [0.2, 0.6, 0.1, 0.1]},
    {'name': 'Technology Innovation', 'expected_return': 0.45, 'risk_factors': [0.3, 0.2, 0.8, 0.1]},
    {'name': 'Operational Efficiency', 'expected_return': 0.15, 'risk_factors': [0.1, 0.1, 0.1, 0.7]}
]

capital_optimizer = CapitalAllocationOptimizer(
    investment_opportunities=investment_opportunities,
    risk_tolerance=0.3  # Moderate risk tolerance
)

allocation_constraints = {
    'min_allocations': [1000000, 500000, 500000, 200000],  # Minimum investments
    'max_allocations': [5000000, 3000000, 2000000, 1000000],  # Maximum investments
    'max_single_allocation': 4000000  # Maximum single investment
}

optimal_capital_allocation = capital_optimizer.optimize_portfolio(
    total_capital=10000000,  # $10M total capital
    constraints=allocation_constraints
)

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class HumanResourceOptimizer:
    """
    Human resource allocation optimization for strategic objectives
    """

    def __init__(self, strategic_initiatives: list, available_talent: dict):
        self.initiatives = strategic_initiatives
        self.available_talent = available_talent

    def optimize_talent_allocation(self, success_requirements: dict) -> dict:
        """
        Optimize talent allocation across strategic initiatives
        """
        n_initiatives = len(self.initiatives)
        n_talent_types = len(self.available_talent)

        # Decision variables (talent allocation matrix)
        allocation_matrix = cp.Variable((n_initiatives, n_talent_types), nonneg=True)

        # Initiative success probability based on talent allocation
        success_probabilities = []

        for i, initiative in enumerate(self.initiatives):
            required_skills = initiative['required_skills']
            initiative_success = 0

            for j, (talent_type, available_count) in enumerate(self.available_talent.items()):
                skill_weight = required_skills.get(talent_type, 0)
                initiative_success += skill_weight * allocation_matrix[i, j]

            # Diminishing returns on team size
            team_size = cp.sum(allocation_matrix[i, :])
            efficiency_factor = 1 / (1 + 0.1 * team_size)  # Efficiency decreases with team size

            success_probabilities.append(initiative_success * efficiency_factor)

        # Objective: Maximize weighted initiative success
        initiative_weights = [init['strategic_weight'] for init in self.initiatives]
        total_success = sum(weight * prob for weight, prob in zip(initiative_weights, success_probabilities))

        objective = cp.Maximize(total_success)

        # Constraints
        constraints = []

        # Talent availability constraints
        for j, (talent_type, available_count) in enumerate(self.available_talent.items()):
            constraints.append(cp.sum(allocation_matrix[:, j]) <= available_count)

        # Minimum team size constraints
        for i, initiative in enumerate(self.initiatives):
            min_team_size = initiative.get('min_team_size', 2)
            constraints.append(cp.sum(allocation_matrix[i, :]) >= min_team_size)

        # Maximum team size constraints
        for i, initiative in enumerate(self.initiatives):
            max_team_size = initiative.get('max_team_size', 20)
            constraints.append(cp.sum(allocation_matrix[i, :]) <= max_team_size)

        # Solve optimization
        problem = cp.Problem(objective, constraints)
        problem.solve()

        if problem.status == cp.OPTIMAL:
            optimal_allocation = allocation_matrix.value

            # Calculate results
            initiative_results = []
            for i, initiative in enumerate(self.initiatives):
                team_allocation = optimal_allocation[i, :]
                total_team_size = np.sum(team_allocation)

                initiative_results.append({
                    'initiative': initiative['name'],
                    'team_allocation': {
                        talent_type: int(team_allocation[j])
                        for j, talent_type in enumerate(self.available_talent.keys())
                    },
                    'total_team_size': int(total_team_size),
                    'expected_success_probability': float(success_probabilities[i].value)
                })

            return {
                'initiative_allocations': initiative_results,
                'total_success_score': float(total_success.value),
                'optimization_status': 'optimal',
                'resource_utilization': {
                    talent_type: float(np.sum(optimal_allocation[:, j])) / available_count
                    for j, (talent_type, available_count) in enumerate(self.available_talent.items())
                }
            }
        else:
            return {
                'optimization_status': problem.status,
                'error': 'Optimization failed'
            }

# Example: Strategic initiative resource allocation
strategic_initiatives = [
    {
        'name': 'AI Platform Development',
        'strategic_weight': 0.35,
        'required_skills': {'senior_engineers': 0.4, 'data_scientists': 0.3, 'product_managers': 0.2, 'designers': 0.1},
        'min_team_size': 8,
        'max_team_size': 15
    },
    {
        'name': 'Market Expansion',
        'strategic_weight': 0.25,
        'required_skills': {'sales_leaders': 0.3, 'marketing_managers': 0.3, 'product_managers': 0.2, 'senior_engineers': 0.2},
        'min_team_size': 5,
        'max_team_size': 12
    },
    {
        'name': 'Operational Excellence',
        'strategic_weight': 0.20,
        'required_skills': {'operations_managers': 0.4, 'senior_engineers': 0.3, 'data_scientists': 0.2, 'product_managers': 0.1},
        'min_team_size': 4,
        'max_team_size': 10
    },
    {
        'name': 'Customer Success Enhancement',
        'strategic_weight': 0.20,
        'required_skills': {'customer_success_managers': 0.4, 'product_managers': 0.3, 'designers': 0.2, 'senior_engineers': 0.1},
        'min_team_size': 3,
        'max_team_size': 8
    }
]

available_talent_pool = {
    'senior_engineers': 25,
    'data_scientists': 12,
    'product_managers': 8,
    'designers': 6,
    'sales_leaders': 5,
    'marketing_managers': 7,
    'operations_managers': 4,
    'customer_success_managers': 10
}

resource_optimizer = HumanResourceOptimizer(
    strategic_initiatives=strategic_initiatives,
    available_talent=available_talent_pool
)

optimal_resource_allocation = resource_optimizer.optimize_talent_allocation({
    'min_success_probability': 0.70,
    'resource_utilization_target': 0.85
})

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import networkx as nx
from datetime import datetime, timedelta

class TimelineOptimizer:
    """
    Timeline and milestone optimization using critical path method
    """

    def __init__(self, strategic_initiatives: list):
        self.initiatives = strategic_initiatives
        self.dependency_graph = nx.DiGraph()

    def build_dependency_graph(self, dependencies: dict):
        """
        Build initiative dependency graph
        """
        # Add nodes (initiatives)
        for initiative in self.initiatives:
            self.dependency_graph.add_node(
                initiative['id'],
                duration=initiative['estimated_duration'],
                resources=initiative['required_resources'],
                priority=initiative['strategic_priority']
            )

        # Add edges (dependencies)
        for initiative_id, deps in dependencies.items():
            for dep_id in deps:
                self.dependency_graph.add_edge(dep_id, initiative_id)

    def optimize_timeline(self, resource_constraints: dict, target_completion: datetime) -> dict:
        """
        Optimize timeline considering resource constraints and dependencies
        """
        # Calculate critical path
        try:
            critical_path = nx.dag_longest_path(self.dependency_graph, weight='duration')
            critical_path_length = nx.dag_longest_path_length(self.dependency_graph, weight='duration')
        except nx.NetworkXError:
            return {'error': 'Circular dependency detected in initiative graph'}

        # Resource leveling optimization
        schedule = self._resource_leveling_optimization(resource_constraints, target_completion)

        # Calculate timeline metrics
        total_duration = max(schedule.values()) if schedule else 0
        resource_utilization = self._calculate_resource_utilization(schedule, resource_constraints)

        return {
            'critical_path': critical_path,
            'critical_path_duration': critical_path_length,
            'optimized_schedule': schedule,
            'total_duration': total_duration,
            'resource_utilization': resource_utilization,
            'timeline_efficiency': critical_path_length / total_duration if total_duration > 0 else 0,
            'completion_date': target_completion + timedelta(days=total_duration)
        }

    def _resource_leveling_optimization(self, resource_constraints: dict, target_completion: datetime) -> dict:
        """
        Resource leveling optimization using heuristic algorithm
        """
        schedule = {}
        resource_usage = {resource: [0] * 365 for resource in resource_constraints.keys()}  # Daily usage

        # Topological sort to ensure dependencies
        try:
            sorted_initiatives = nx.topological_sort(self.dependency_graph)
        except nx.NetworkXError:
            return {}

        for initiative_id in sorted_initiatives:
            initiative_data = self.dependency_graph.nodes[initiative_id]
            duration = initiative_data['duration']
            required_resources = initiative_data['resources']

            # Find earliest start date considering dependencies and resource constraints
            earliest_start = self._find_earliest_start_date(
                initiative_id, schedule, resource_usage, resource_constraints, required_resources, duration
            )

            schedule[initiative_id] = earliest_start

            # Update resource usage
            for day in range(earliest_start, earliest_start + duration):
                if day < len(resource_usage[list(resource_constraints.keys())[0]]):
                    for resource, daily_need in required_resources.items():
                        if resource in resource_usage:
                            resource_usage[resource][day] += daily_need

        return schedule

    def _find_earliest_start_date(self, initiative_id: str, current_schedule: dict,
                                resource_usage: dict, resource_constraints: dict,
                                required_resources: dict, duration: int) -> int:
        """
        Find earliest start date considering dependencies and resource constraints
        """
        # Consider dependencies
        min_start_from_deps = 0
        for predecessor in self.dependency_graph.predecessors(initiative_id):
            if predecessor in current_schedule:
                pred_duration = self.dependency_graph.nodes[predecessor]['duration']
                min_start_from_deps = max(min_start_from_deps,
                                        current_schedule[predecessor] + pred_duration)

        # Find earliest start considering resource constraints
        start_day = min_start_from_deps

        while start_day < 365:  # Max planning horizon
            resource_feasible = True

            # Check if resources are available for the entire duration
            for day in range(start_day, min(start_day + duration, 365)):
                for resource, daily_need in required_resources.items():
                    if resource in resource_constraints:
                        if (resource_usage[resource][day] + daily_need >
                            resource_constraints[resource]):
                            resource_feasible = False
                            break
                if not resource_feasible:
                    break

            if resource_feasible:
                return start_day

            start_day += 1

        return start_day

# Example: Strategic timeline optimization
strategic_initiatives = [
    {'id': 'product_dev', 'estimated_duration': 90, 'required_resources': {'engineers': 8, 'designers': 2}, 'strategic_priority': 1},
    {'id': 'market_research', 'estimated_duration': 30, 'required_resources': {'analysts': 3, 'managers': 1}, 'strategic_priority': 2},
    {'id': 'go_to_market', 'estimated_duration': 60, 'required_resources': {'marketers': 4, 'sales': 3}, 'strategic_priority': 3},
    {'id': 'operational_setup', 'estimated_duration': 45, 'required_resources': {'operations': 2, 'engineers': 2}, 'strategic_priority': 2}
]

initiative_dependencies = {
    'product_dev': ['market_research'],
    'go_to_market': ['product_dev'],
    'operational_setup': ['product_dev']
}

resource_constraints = {
    'engineers': 12,
    'designers': 4,
    'analysts': 5,
    'managers': 3,
    'marketers': 6,
    'sales': 5,
    'operations': 3
}

timeline_optimizer = TimelineOptimizer(strategic_initiatives)
timeline_optimizer.build_dependency_graph(initiative_dependencies)

target_completion_date = datetime(2024, 12, 31)
optimized_timeline = timeline_optimizer.optimize_timeline(
    resource_constraints=resource_constraints,
    target_completion=target_completion_date
)